1. Information. Name: Dennis Akos Phone: Contents

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1 University of Colorado Department of Aerospace Engineering Sciences ASEN 4018 Remote Autonomous Mapping of Radio Frequency Obstruction Devices (RAMROD) Concept Definition Document Monday 2 nd October, Project Customers 1. Information Name: Dennis Akos dma@colorado.edu Phone: Team Members Name: Baertsch, Jorgen Jorgen.Baertsch@colorado.edu Phone: (218) Name: Harrmann, Kennedy Kennedy.Harrmann@colorado.edu Phone: (920) Name: Larson, Sarah Sarah.L.Larson@colorado.edu Phone: (720) Name: Morgan, Ethan Ethan.Morgan@colorado.edu Phone: (617) Name: Ursetta, Jake Jake.Ursetta@colorado.edu Phone: (303) Name: Williams, Samantha sawi1734@colorado.edu Phone: (720) Name: Cooke, Ian Ian.Cooke@colorado.edu Phone: (914) Name: Landis, Mary Mary.Landis@colorado.edu Phone: (970) Name: Mast, Harrison Harrison.Mast@me.com Phone: (713) Name: Stout, Selby Selby.Stout@colorado.edu Phone: (443) Name: Williams, Justin Justin.D.Williams@colorado.edu Phone: (720) Name: Phone: Contents 1 Information Project Customers Team Members Table of Acronyms 3 3 Problem Description Mission Statement Problem Statement Specific Objectives Concept of Operations (CONOPS) Functional Block Digram (FBD) Design Requirements 7

2 5 Key Design Options Considered UAS Selection Multi-Rotor Fixed wing Blimp Summary Autopilot Software Proprietary Autopilot Open-Source Autopilot Ground-Up Autopilot Localization Method Time Difference of Arrival Method Angle of Arrival Method Power Difference of Arrival Method Triangulation Methods of GPS Denied Guidance Dead Reckoning with Inertial Navigation System LiDAR Based Localization and Mapping LTE Localization Payload Processing Unit Microprocessor (MPU) Microcontroller(MCU) Trade Study Processes and Results Autopilot Software Localization Method Methods of GPS Denied Guidance Trade Metrics Trade Results Payload Processing Unit Trade Study Metrics Trade Study Results Selection of Baseline Design UAS Algorithm Autopilot Software Localization Method Methods of GPS Denied Guidance Operational Payload Monday 2 nd October, of 38

3 2. Table of Acronyms Acronym AGC ALU AP BTS CONOPS COTS CPE CPU DR ecid enb ET FAA FBD FR GNC GNSS GPS IC IMU INS IRISS LiDAR LS MCU MPU OP OTDOA PDOA PPD PES RFIL RFI SLAM TDOA UAS UE Definition Automatic Gain Control Arithemetic Logic Unit AutoPilot Base Transceiver Station CONcept of OPerationS Commercial Off-The-Shelf Critical Project Element Central Processing Unit Derived Requirement enhanced Cell ID evolved Node B Emerging Threat Federal Aviation Administration Functional Block Diagram Functional Requirement Guidance, Navigation, and Control Global Navigation Satellite System Global Positioning System Integrated Circuit Inertial Measurement Unit Inertial Navigation System Integrated Remote and In Situ Sensing Light Detection and Ranging Location service client Server Microcontroller Microprocessor Operational Payload Observed Time Difference of Arrival Power Difference Of Arrival Personal Privacy Device Personal privacy device and Emerging threat Sources Radio Frequency Interference Localization Radio Frequency Interference Simultaneous Localization And Mapping Time Difference Of Arrival Unmanned Aerial System User Equipment Monday 2 nd October, of 38

4 3.1. Mission Statement 3. Problem Description RAMROD (Remote Autonomous Mapping of Radio Frequency Obstruction Devices) will utilize an autonomous UAS and self-contained sensor payload to localize RFI and ET sources in a GPS-denied environment to allow civilian and military GNSS endeavors to continue without disruption Problem Statement Global Navigation Satellite Systems, such as the US Global Positioning System (GPS), are radio frequency based navigation tools capable of providing devices with accurate positioning data anywhere on earth. In today s world, GPS is relied on heavily for navigation in aircraft and automobiles. In addition, GPS has become an important tool/method in autonomous systems, which are vehicles that can navigate their environment without human input. Recently, devices capable of overloading or mimicking GPS signals have become more and more common. These devices are known as Personal Privacy Devices (PPDs) and Emerging Threat (ET) spoofing devices. PPDs operate by broadcasting noise on one or both GPS frequencies, and ETs broadcast a false set of GPS coordinates meant to convince a UAS that it is in a different location. They are small in size, low cost, and their high availability poses a threat to the continued use of GPS for navigation. In order to ensure that GPS signals are not tampered with and that systems reliant on GPS can operate uninterrupted, it s imperative to find and disable them as quickly as possible. One way to locate these devices is by using aerial mapping of radio frequency signals on flight systems over large areas. However, these types of flight systems and aerial mapping systems are reliant on GPS signal for navigation and mapping. In order to use one, a positioning system and an autopilot capable of accurate positioning and navigating through GPS denied conditions is necessary. The purpose of the RAMROD project is to design, fabricate, and test a self-contained operational payload for a Group 4 military UAS that is capable of detecting and localizing large-scale Radio Frequency Interference (RFI) sources while operating in GPS-denied conditions. In order to complete this objective, a technique to provide accurate positioning data in GPS-denied conditions will be necessary. Additionally, a localization algorithm for finding these RFI sources will be needed. Unfortunately, it is not possible to obtain a Group 4 military UAS to test the operational payload for this project; therefore, another objective of the project is to design, build, and test a small UAS capable of localizing a small-scale RFI source. The purpose of this small scale UAS is to simulate the environment of the operational payload and test the concept of positioning and mapping in GPS denied conditions Specific Objectives In order to achieve minimum success, the operational payload must be designed, fabricated, and tested. This payload must have modified software for RFI localization in GPS denied environments and all necessary components to make the system self sufficient including modules for power, data processing, data transmission, and RFI sensing. The payload must be capable of operating under its own power for at least 24 hours, have real-time data transmission, and contain a suite of navigation sensors functionally enabled by the implementation of a PDOA and GPS denied algorithm. This algorithm will be designed for both localization of RFI sources and for position tracking during GPS denied conditions. Furthermore, it must have the ability to be integrated with a Group 4 military UAS. This minimum success also includes developing a UAS flight system to implement the PDOA algorithm and extended flight software to allow the UAS to fly in GPS-denied or spoofed conditions. Since the operational payload will not be tested on a UAS in this project due to testing constraints, the sole purpose of the RAMROD UAS is to provide validation for the PDOA and GPS denied algorithm, since this same algorithm will be be employed on the operational payload. In order to locate PPDs and ETs, the UAS must be capable of flying for at least 60 minutes and maintaining steady level flight for at least 1km in a simulated GPS denied environment. This UAS will be used exclusively to test the capability of the RF localization and flight software. The self-contained operational sensor package as well as the coupled RF localization and GPS denied flight algorithm will be the final deliverables for the customer. The levels of success for the project are shown in Table 1. Monday 2 nd October, of 38

5 Level 1 Table 1. Levels of Success Operational Payload UAS Platform RF Localization Flight Software a) The sensor package a) The localization algorithm shall be below a TBD shall be able to weight. establish a RFI power b) The operational profile with GPS active. payload shall be self contained in a stable structure. c)the Operational Payload shall establish a RFI power profile without any interface with other systems in a GPS denied environment. a)the UAS shall have a total flight time of 60 min or more. b) The UAS shall maintain steady level flight over a linear distance of 1km without GPS. c) The UAS shall fly on a preprogrammed, autonomous flight plan at a 45 meter altitude. a) The flight algorithm shall allow UAS flight in GPS denied environments a for a distance of 1km Level 2 a) The sensor package shall transmit data up to 7.1km. b) The sensor package shall adhere to structural requirements given in MIL-STD a) The UAS shall fly in GPS denied environment for a total time of 10 minutes. b) UAS flight shall cover a 5km x 5km search area. c) The UAS shall be able to fly in high winds (35-40 km/h). a) The localization algorithm shall be able to establish an RFI power profile in a GPS denied environment. b) Localize RFI source within a 40m radius. a) The autopilot shall switch seamlessly between GPS and non GPS flight. Level 3 a) The UAS shall have the ability of stable, autonomous flight with dynamic way-points. b a) The localization algorithm shall integrate with the flight algorithm to allow for a dynamic waypoint flight plan. a) The flight algorithm shall integrate with the RF localization algorithm to allow for flight using dynamic waypoints. a Due to legality, GPS denied environments will be simulated by suspending use of the UAS s GPS functions, and PPD/ET signals will be simulated with WiFi or other signals. b Dynamic way-points indicate the ability of the autopilot to autonomously alter its flightpath based off of incoming data 3.4. Concept of Operations (CONOPS) The CONOPS for this system is shown in Figure 1. This project requires three deliverables; the operational payload, an algorithm for identifying position and flying a UAS in a GPS denied environment, and a PDOA based algorithm for localizing an RFI source. The UAS will serve as a test bed to prove functionality of the GPS denied autopilot and localization software, and will be flown on a pre-programmed flight path. The operational payload implements the working software base on a system that is stand-alone and does not require any interface with additional components. The operational payload will not be flown on a UAS during this project, because it is outside the scope of this project. Instead, the payload will be walked or driven around a path similar to that of the UAS in order to localize the same PPD/ET. Once both sets of data have been obtained, they will be compared to validate that the operational payload is more accurate. The operational payload will be entirely self contained including sensors, a power source, a form of communication to the ground station, and an on board computer. Monday 2 nd October, of 38

6 Figure 1. RFI Concept of Operations 3.5. Functional Block Digram (FBD) Figure 2 shows the functional block diagram for the full system design, including components designed by this project and components either supplied by the customer, or bought commercial off the shelf (COTS). The main objective of this project will primarily be completed by components located on the UAS. The UAS platform will be created using a COTS airframe with open-source flight software to enable necessary modifications. The operational payload, while designed for flight capability, will not necessarily be implemented on the UAS which is demonstrated by the lack of connection between the payload and the rest of the system. The operational payload must be fully functional without any interface with a UAS and must contain all necessary equipment needed to make the RFI measurements and localization while being self-contained. The UAS, and all interfacing components, are necessary for the ability to fly and show accurate RFI localization in GPS denied or spoofed environments. The operational payload serves to demonstrate that a single unit can accomplish the overall objectives of the mission without any additional components. Monday 2nd October, of 38

7 Figure 2. RFI Monitoring Functional Block Diagram 4. Design Requirements Functional Requirement FR 1 FR 2 FR 3 FR 4 FR 5 FR 6 FR 7 FR 8 FR 9 FR 10 FR 11 Table 2. Functional Requirements Description The UAS shall have a flight time of 60 minutes The UAS shall fly in maximum winds of 40 km/hr The UAS shall fly in a GPS denied environment for a linear distance of up to 1 km The UAS shall support all flight hardware and instrumentation The UAS and its testing shall adhere to FAA and CU Boulder regulations The system shall fly autonomously given a pre-programmed flight plan The system shall have the ability to seamlessly switch between GPS and GPS-denied flight The system shall generate metrics to assess flight performance The system shall create a profile of RF signal power The payload components shall be in a stable self-contained structure The payload shall have the ability to measure and localize an RFI source in GPS denied environments FR 1: The UAS shall have a flight time of 60 minutes. Motivation: This is a level 1 requirement from the customer that must be met for minimum success. Having a flight time of 60 minutes will allow enough time for the UAS to collect data over the specified area. Verification: Test - A flight test will be conducted in order to determine the maximum flight time of the UAS. DR 1.1: The UAS shall be a fixed-wing aircraft. Motivation: A fixed wing will offer longer flight times and heavier payload capacity. A fixed wing will also be able to cover a larger area during the flight test. Additionally, the customer has also requested a fixed wing aircraft rather than a multi-rotor or other aerial vehicle, as this will closely simulate the operational payload environment. Monday 2 nd October, of 38

8 Verification: Inspection - The UAS can be visually confirmed as a fixed-wing aircraft. DR 1.2: The UAS propulsion system shall be electrically powered. Motivation: This is a requirement given by the customer, given its versatility, reliability, and ease of use. Additionally, electric power is a standard method used to power civilian UAVs, providing much research and many resources on the topic. Verification: Inspection - The power system chosen will be electric. This can be confirmed through specification sheets for the chosen power source. DR 1.2.1: The electric power source shall be a battery. Motivation: A battery was chosen as the power source because it offered the cheapest power solution for the smallest weight and cost. Batteries also offer the ability to be recharged and reused for many cycles. Verification: Inspection - The power source will be a battery. DR 1.2.2: The battery shall have a minimum power capacity capable of powering the UAS with all hardware and instrumentation for at least 60 minutes. Motivation: This capacity will come from a power required calculation. A preliminary derivation can be found in the appendix. Verification: Inspection - The power output of the battery can be determined by looking at its specification sheet. FR 2: The UAS shall fly in maximum winds of 40 km/h. Motivation: This is a level 1 requirement from the customer that must be met for minimum success. The value set for high winds is based off the wind speed that the Parrot Disco can withstand, which is considered the minimum baseline since the Disco was used for all previous work. Verification: Test - The UAS will be flown in a high wind environment to verify that it can function correctly and still localize the RFI source. DR 2.1: The UAS shall have a maximum speed of at least 45 km/h. Motivation: When the UAS is flying in maximum wind conditions, with a direct head wind, it needs to be capable of covering some ground, rather than flying backwards or standing still. Verification: Analysis/Test - This will be verified by manufacturer specifications of the chosen airframe, as well as thrust and drag calculations for the chosen propulsion system. This can additionally be verified through telemetry data from a UAS flight. FR 3: The UAS shall fly in a GPS denied environment for a linear distance of up to 1 km. Motivation: The UAS is required by the customer to fly in a GPS denied environment. The GPS denied environment is defined as a 500 m radius around the RFI source. The longest distance in this environment is directly across the center of the circle for a distance of 1 km. Verification: Test - A flight test will be conducted where GPS signals are removed. DR 3.1: The UAS shall contain a full inertial sensor package. Motivation: The inertial sensor package will allow the UAS the ability to continue to fly in a GPS denied environment. Additionally, the sensor package will enable the UAS to track its position in inertial space, giving a more accurate location of any RFI devices. Verification: Inspection - An inertial sensor package will be included in the design, and integrated into the flight software of the UAS. DR 3.2: The drift of the UAS in the Z direction (with the Z axis representing altitude) shall remain under 10 m. Motivation: The UAS is likely to drift horizontally when the GPS signal is lost, due to winds and inaccuracies in the inertial navigation method. If the UAS drifts in altitude more that 10 m, there is a possibility that the UAS could collide with an object on the ground, the ground itself, or go above the regulated FAA altitude, depending on the stage of flight and original altitude. Verification: Test/Analysis - This will be built into the system itself, with an accurate GPS denied GNC method, which will be verified through the analysis of telemetry data from UAS flight tests. FR 4: The UAS shall support all flight hardware and instrumentation. Motivation: In order to be fully functional the UAS must contain all necessary hardware and instrumentation for flight and data collection. Verification: Inspection - All necessary components will be contained within the UAS. Monday 2 nd October, of 38

9 DR 4.1: All flight hardware and instrumentation shall fit in the fuselage. Motivation: This is necessary for safe and controlled flight. The only exception to this requirement is antennas, which must remain outside the fuselage for proper functionality. Verification: Inspection - This will be visually inspected prior to all flights. DR 4.2: The UAS shall have interfaces for all flight hardware and instrumentation. Motivation: This ensures that all sub-systems may work together and no components will become detached during flight Verification: Inspection - Interfaces will be built into the UAS design for every required component. DR 4.3: The UAS shall keep all hardware and instrumentation within their operating temperatures. Motivation: This ensures that all hardware and instrumentation function properly. Verification: Test - Ground tests will be conducted before any flight testing to verify that instruments remain within their operating temperature. There will also be a temperature sensor on-board that will give real time temperature data during flight. FR 5: The UAS and its testing shall adhere to Federal Aviation Administration (FAA) and University of Colorado Boulder regulations. Motivation: In order to legally fly in any US airspace the UAS must adhere to all regulations set forth by the FAA. To test on CU Boulder property the UAS must also adhere to all CU boulder regulations Verification: All regulations will be written into test procedures prior to testing and any registration/clearance required to fly at a particular site will be verified prior to testing. DR 5.1: The UAS shall weigh less than 25 kg. Motivation: This is an FAA regulation for any civilian UAS. Verification: Inspection - The UAS will be weighed prior to operation to ensure it is under the regulation weight. DR 5.2: The UAS pilot shall be certified by the FAA. Motivation: This is an FAA regulation. Verification: Inspection - The UAS pilot will present their FAA certification prior to any flight. DR 5.3: The UAS pilot shall be certified by CU Boulder. Motivation: With a CU Boulder certified pilot, the team will be covered by the University s liability insurance should any incidents occur. Verification: Inspection - The UAS pilot will present their CU Boulder certification prior to any flight. DR 5.4: The UAS pilot shall be able to take full control of the UAS at any time during the test. Motivation: This is an FAA regulation for all civilian UAS. Verification: Test - This will be be a feature built into the flight software such that a switch can be flipped on the ground based flight controller to regain manual control in case of emergency. This function will be tested during a UAS test flight. DR 5.5: The UAS shall remain within visual line of sight during all times. Motivation: This is an FAA regulation for all civilian UAS. Verification: Inspection - Based on research, the average line of sight for safe and controllable flight is 2 km in any direction. During any UAS flight, the pilot will never be more than 2 km away from the UAS. DR 5.6: The UAS shall not fly within 8.05 km (5 mi) of an airport. Motivation: This is an FAA regulation for all civilian UAS. Verification: Inspection - Prior to any UAS flight, the testing area will be determined to be at least 8.05 km from the nearest airport. DR 5.7: The UAS shall fly at an altitude below 121 m (400 ft) above ground level. Motivation: This is an FAA regulation for all civilian UAS. Verification: Test - The pre-programmed flight path will specify a flight altitude below 121 m above ground level, which can be verified by telemetry data. Additionally, the flight will be conducted at an altitude far below the FAA regulated altitude for accurate RFI power measurements. FR 6: The UAS system shall fly autonomously given a pre-programmed flight plan. Motivation: Previous work has been done locating an RFI source using multiple ground stations and triangulation, but an autonomous UAS would be advantageous for efficiency and flying in environments which may Monday 2 nd October, of 38

10 block RFI signals to the ground stations, such as locations with buildings or land masses. Since the UAS will be flying over a designated area, pre-programming a flight plan will allow the UAS to cover the region in the most efficient way possible. Verification: Test/Demonstration - A test flight will be conducted in order to confirm that the UAS will fly along the programmed path. DR 6.1: The UAS system shall be able to fly autonomously with access to GPS data. Motivation: Most autopilots rely heavily on GPS for position determination due to its high accuracy. Although the UAS will be flying in intermittent regions where GPS is denied, GPS will still be used for guidance for most of the flight path. This will allow the autopilot to determine the UAS position with the most accuracy and to recalibrate the inertial sensors after exiting a GPS denied environment. Verification: Demonstration - The UAS will successfully navigate in a GPS enabled environment without human control input. DR 6.1.1: The UAS flight algorithm will use pre-programmed way points and speeds. Motivation: Way points are a commonly used method of programming flight paths for UASs. Since the UAS will be flying over a designated region, way points can easily be placed by the pilot in order to cover the region in the most effective and efficient way. Since the UAS will be measuring RF power levels at a chosen sample rate, it is important to regulate speed in order to maintain the accuracy of the RF sensors. Verification: Demonstration and Test - The UAS will successfully navigate a flight path set by preprogrammed way points and speeds, using GPS for positioning. Flight data will then be analyzed to verify accuracy. DR 6.1.2: The UAS flight system shall be able to withstand high winds, as defined by FR 2, while using a preprogrammed flight plan. Motivation: In order to fly and navigate accurately for an extended period of time, the UAS autopilot will have to accommodate for natural disturbances and forces. The most notable external force will be from the wind acting on the UAS. Verification: Demonstration and Test - The flight system will maintain control of the UAS in wind speeds of up to 40 km/h. Flight data will be analyzed and compared to expected flight path. DR 6.2: The UAS flight system shall be able to fly autonomously without access to GPS data. Motivation: Since the UAS will be used in order to detect PDDs and ETs, it will be flying in regions where GPS data is unavailable. This will cause a standard autopilot to malfunction, leading the UAS to either crash or stray from the original flight plan. To compensate for this, the autopilot will have to capable of maintaining flight in both GPS and GPS denied environments. Verification: Demonstration - The UAS will maintain flight without relying on GPS data. DR 6.2.1: The UAS flight algorithm shall be capable of ignoring the incoming GPS data. Motivation: When the UAS is flying in a simulated GPS-denied environment, it will still be receiving GPS data since it is illegal to jam GPS signal. When within the range of a RFI or ET source the GPS data will be considered unusable due to interference and could potentially be misleading if an ET is being used to broadcast false GPS signals. Utilizing these signals could cause the UAS to stray from its flight plan or to crash, therefore the flight algorithm for GPS denied flight must not use GPS data when in the simulated GPS denied environment. Verification: Simulation - The algorithm will execute the GPS-denied routine when instructed to do so, effectively ignoring GPS input. This will also be demonstrated during the full flight test. DR 6.2.2: The UAS shall fly straight and level until GPS signal is regained. Motivation: Autonomous flight without GPS will be accomplished through the use of inertial sensors. Relying solely on inertial sensors for an extended time period can be problematic as they will develop navigational inaccuracies and cause the UAS to drift. For that reason, the team will initially only be maintaining straight and level flight in a GPS denied zone to increase localization accuracy of the RFI source. Verification: Demonstration and Test - The flight system will hold the UAS at a fixed altitude and heading while flying without GPS. Flight data will then be analyzed with a flight path map to verify accuracy. Monday 2 nd October, of 38

11 DR 6.2.3: The UAS flight algorithm shall provide a flight path based on onboard inertial sensors. Motivation: In order to determine the UAS position without GPS, inertial sensors will be used. These inertial sensors will be used to track and log the UAS flight path. This path can then be compared to one generated with GPS in order to identify and minimize any inaccuracies caused by relying on inertial sensors. Verification: The flight path provided by inertial sensors will be compared in post-processing to the original pre-programmed flight path in order to assess accuracy. FR 7: The UAS flight algorithm shall have the ability to seamlessly switch between GPS enabled and GPS denied flight. Motivation: The algorithm must be able to integrate with the RF sensors to determine when it can no longer trust the GPS data, then switch to the GPS denied flight mode in such a way that does not jeopardize control of the UAS. This process will take place when a set power threshold is exceeded by the AGC sensor, and must occur in a way that will prevent the UAS from straying from its flight path or crashing. Verification: Test - The UAS will switch between GPS enabled and GPS denied flight without deviating from steady, level flight. DR 7.1: The UAS flight system shall integrate with RF sensors in order to monitor RF power levels in real time. Motivation: RF sensors will be on the UAS for the purpose of RFI localization, however these sensors will also be utilized in the flight software. In order to determine when the UAS is entering an area where GPS data is denied or falsified, RF power levels can be used as an indicator. A power level that exceeds a certain threshold will trigger the switch between flight modes. This can only be done if the the flight software integrates with the RF sensors and monitors the data they collect throughout the entire flight. Verification: Test - The algorithm will take continuous measurements of RF power and feed into the autopilot software. DR 7.1.1: The UAS flight algorithm shall measure power on both GPS L1 and L2 frequency bands. Motivation: GPS communication utilizes two main frequency bands in order to send and receive signals. In order to detect any GPS signal or interference the system must be monitoring data on both bands. Verification: Test - The RF sensors will collect data on the GPS L1 band with a center frequency of MHz and the GPS L2 band with a center frequency of MHz. DR 7.2: The UAS flight algorithm shall utilize an RF signal power threshold in order to trigger a switch in flight modes. Motivation: PPDs generate signal interference by adding excess noise to GPS frequency bands. This causes the information that the original signals carried to be skewed and also cause the power of the signals to increase. Since the power levels detected will increase for signals that are being affected by RFI, a power threshold can be utilized in order to determine when the UAS is in a region being effected by RFI. Verification: Test/Demonstration - System will autonomously switch between GPS/no GPS flight when threshold is reached. DR 7.2.1: The UAS flight algorithm shall switch to GPS denied flight when power threshold is exceeded. Motivation: When the system detects an increase in power above the threshold, the UAS must be able to switch over automatically to flying using inertial sensors. Verification: Test/Demonstration - System shall maintain steady, level flight while autonomously switching between GPS/no GPS flight. DR 7.2.2: The UAS flight algorithm shall switch back to GPS flight when power levels drop below power threshold. Motivation: The system will be constantly sampling RF power, and must be able to switch back to using GPS to aid flight when the signal is available and reliable. Verification: Test/Demonstration - System shall maintain steady, level flight while autonomously switching between GPS/no GPS flight. DR : The UAS flight algorithm shall correct path and recalibrate inertial sensors once access to GPS is regained. Motivation: Flying using inertial sensors will inevitably cause drift in the flight path. Thus, when GPS becomes available, it will be necessary to correct for this accumulated error. Verification: Test/Demonstration - The system will accurately return to the original flight path and recalibrate inertial sensors once GPS is regained. Monday 2 nd October, of 38

12 DR 7.3: The UAS flight algorithm shall be able to function correctly in the presence of both PPDs and ETs. Motivation: There are two main methods of creating RFI. One is a PPD, which adds excess noise to GPS signals to produce uninterpretable data, essentially rendering GPS in the effected areas useless. The other, Emerging Threats, or ETs, are GPS signals that have been spoofed to transmit false coordinates. In order to be effective for all cases, the algorithm must be able to function regardless of the type of interference present. Verification: Test/Demonstration - Testing using actual PPDs and ETs is illegal in the US. Therefore, the system will be tested using simulated GPS-denied conditions, both with excess noise and with falsified coordinates. FR 8: The UAS flight algorithm shall record data to assess flight performance. Motivation : To determine the performance of the UAS in the GPS denied flight mode and to be able to localize the RFI signal, the algorithm must generate data for quantifying errors in the UASs position data. Verification: Analysis - The GPS positional data will be compared with the data from inertial sensors in order to quantify errors. DR 8.1: The UAS flight algorithm shall output GPS data which will be compared with the estimated flight path generated by inertial sensors. Motivation: To determine the accuracy of the flight path generated through inertial sensors the path must be compared to an accurate reference. Since GPS is the most accurate form of position determination available, it will be compared with the flight path generated through inertial sensors in order to determine the error. Verification: Test - The UASs true position will be tracked during flight and compared to estimated positions. FR 9: The UAS flight algorithm shall create a profile of RF signal power. Motivation: In order to locate an RFI source, RF power levels must constantly be monitored during the flight. This will allow the system to develop an RF power level profile for the area of interest. Analyzing this power profile and determining where the strongest signals are coming from will allow the RFI source s position to be determined. Verification: Test - Data will be analyzed after UAS flight test in order to verify that the UAS is measuring RF signal strength. DR 9.1: The localization algorithm used with both the UAS system and the operational payload shall function using payload hardware only. Motivation: The localization algorithm will be utilized on the self contained operational payload. Since this payload is meant to function without integrating with a UAS, the algorithm must be fully functional while only utilizing hardware contained in the payload. Verification: Test - Algorithm will be used with the operational payload to localize and RFI source during a ground test. DR 9.2: The localization algorithm shall locate an RF signal source within a 4km x 4km square area. Motivation: When flying over the test area, the system must be able to locate the position of the PPD/ET device. Verification: Test - The algorithm will accurately localize the source of the RFI. DR 9.2.1: The UAS and operational payload shall determine location within a circular area with a 40 meter radius. Motivation: This accuracy is based on preliminary calculations using other requirements such as flight time and total search area. Localization is crucial to the success of the project because the goal is to find the location of the RFI device. Verification: The algorithm will define the location of the RFI source to within the specified area. DR 9.3: The localization algorithm shall establish an RFI profile map based on power measurements and UAS position data without access to GPS data. Motivation: To determine where an RFI source is located, it is helpful to gain an understanding for the distribution of power over the effected area. Analyzing this data and determining the location where the power is strongest will allow the location of the RFI source to be determined. This can only be done if power measurements and positional data are constantly being monitored and compared to each other. Monday 2 nd October, of 38

13 Verification: Analysis - Data collected from flight and ground tests will be analyzed and plotted to develop an RFI power profile. The power profile can then be compared to expectations and GPS data to determine accuracy. DR 9.3.1: The localization algorithm shall measure signal strength of a WiFi or cellular signal during flight. Motivation: Since it is illegal to use PPDs to interfere with GPS signals, a full test with an actual PPD cannot be conducted. The algorithm will work the same way regardless of the frequency bands being targeted. By using a different signal source that will still create a varying distribution in signal strength, the localization algorithm can still be tested. The most obvious signals to use for testing are WiFi and cellular which means the algorithm must be capable of measuring these frequencies as well as GPS. Verification: Test - Ground and flight tests will be conducted using WiFi or cellular signals. DR 9.4: The localization algorithm shall interface with the flight algorithm during flight to receive positional data. Motivation: For the localization algorithm to develop an RFI power profile, data collected from the RF sensors will need to be plotted against positional data. This positional data will come from the flight algorithm which will provide data with or without GPS. Verification: Analysis - The localization algorithm will output positional data that matches the positional data generated by the flight algorithm. DR 9.4.1: The localization algorithm shall receive positional data from the flight algorithm when GPS data is present. Motivation: Positional data will be acquired and utilized by the flight algorithm. To generate the most accurate power profile, this positional data will be used. This will remove any inconsistencies brought about by taking positional data from multiple sources. Verification: Test/Analysis: Flight and ground tests will be conducted when GPS is present and the generated data will be analyzed. DR 9.4.2: The localization algorithm shall receive positional data from the flight algorithm when GPS data is not present. Motivation: While flying in a GPS denied environment standard methods for position determination cannot be used. In these conditions, the flight algorithm will be using inertial data to determine position. To remain consistent in the data collected, the same positional data will be used for the localization algorithm. Verification: Test/Analysis - Flight and ground tests will be conducted when GPS is denied. DR 9.5: The algorithm shall store data for post-processing. Motivation: All data will be stored to be analyzed once the UAS is on the ground. This will allow the generated power profile to be analyzed and the RFI source to be located. Verification: Analysis - After flight and ground tests are conducted the generated data will be retrieved and analyzed. DR 9.5.1: The algorithm shall store GPS positional data as well as positional data compiled from the inertial sensors. Motivation: The actual positional data must be compared with positional data from inertial sensors in order to determine autopilot accuracy. Verification: Analysis - Once flight and ground tests are conducted the generated data will be retrieved and analyzed. DR 9.5.2: The algorithm shall store RF power measurements. Motivation: An RF profile must be compiled in order to localize the RFI source. Verification: Analysis - Once flight and ground tests are conducted the generated data will be retrieved and analyzed. FR 10: The operational payload components shall be fully functional in a self-contained structure. Motivation: The operational payload will be flying on a military grade Group 4 UAS. Due to military restrictions, the operational payload cannot interface with the UAS and therefore must have the ability to operate on its own, in its own structure, without any non-mechanical interfaces. Verification: Demonstration - A ground test, walking or driving, of the payload will verify independent functionality. Monday 2 nd October, of 38

14 DR 10.1: The weight of the operational payload shall remain below 5.5 kilograms. Motivation: The payload capacity of a Group 4 UAS is approximately 270 kilograms. To not hinder the performance of the vehicle, a goal is to keep the weight under 1/50th of the total payload capacity of the UAS. Verification: Inspection - The payload will be weighed. DR 10.2: The volume of the operational payload shall remain below 32x15x15 centimeters. Motivation: The typical payload of such a Group 4 military UAS is four HELLFIRE missiles with additional payload. The diameter of one of these missiles is about 17 centimeters and the wingspan of the fins is 32 cm. While the dimensions of the military UAS are classified, it is known that this set of dimensions will fit easily within the UAS. Keeping the payload size below these dimensions will ensure that it will fit securely within the UAS and allow for additional payload, like more missiles, to be added without any interference from the operational payload. Verification: Inspection - The final enclosed payload will be measured. DR 10.3: Each structural component of the operational payload shall have a first modal frequency above 100 Hz. Motivation: During flight, the payload cannot vibrate apart or out of place. The military does not publicly specify vibration limits for flight testing; however, NASA has a set value of 100 Hz for launch survival. If the first modal frequency of the structural components is above this value, the payload will survive the entire flight on a group 4 UAS since these flight conditions will never be as severe as launch conditions. Verification: Analysis - Computer Aided Design analysis on the modal frequencies of the components of the structure. DR 10.4: The operational payload structure shall have a mechanical interface to allow for attachment to a baseplate structure. Motivation: While the exact mounting specifications of the military UAS payload bay are not known, it is expected that the operational payload will be mounted to a rigid flat surface. The payload must have the ability to mount to this surface and stay attached regardless of the flight conditions. Verification: Demonstration - The payload will be mounted to a dummy plate that will act as the mounting surface of the military UAS. DR 10.5: The temperature inside the operational payload shall stay within the operating temperature of all electronic components contained. Motivation: If the temperature inside the payload rises or falls to a level that can damage the electronic components, the integrity of the project could be compromised. Verification: Test - The temperature inside the payload will be recorded during operational use. DR : The operational payload shall contain a temperature regulation system. Motivation: The payload needs to have this to ensure that the temperature stays within the operational range of all instruments it contains. Verification: Test - The temperature inside the payload will be monitored during operational use. FR 11: The operational payload shall have ability to measure and localize an RFI source in GPS denied environments. Motivation: This is the main objective of the project and is required for minimum success. Verification: Test and Demonstration: A ground test (walking or driving) of the payload will be used to create a RFI power profile (see CONOPS). DR 11.1: The operational payload shall have its own processing unit. Motivation: Because the payload must be self contained, there must be an on board processing unit to take the raw data from the sensors and give it all meaning in real time. Verification: Inspection - The processing unit will be visually inspected. DR 11.2: The operational payload shall have its own power source. Motivation: The payload will not be able to interface with the UAS so it will need a power source to keep all electronic components operating. Verification: Inspection - The power source will be visually inspected. DR : The operational payload shall operate for at least 24 hours. Motivation: The end user for this project plans to fly the payload in a UAS with a flight time of approximately 24 hours. The 24 hours of operational use should allow for data collection for the large majority of the flight. Verification: Test - The payload will run a test that lasts for 24 hours. Monday 2 nd October, of 38

15 DR 11.3: The operational payload shall have the ability to identify its location without GPS. Motivation: When in a GPS denied environment, the payload will need to have an idea of its location in order to provide accurate data. Verification: Test - Turn off GPS to verify that payload can identify its location. DR 11.4: The operational payload shall have a board capable of measuring RFI power in the GPS band using AGC circuitry. Motivation: The payload needs to localize the signal without interfacing with the UAS. Verification: Test - A ground test will be run using only the payload to verify that it can measure the RFI power. DR : The operational payload shall have an antenna capable of taking these measurements. Motivation: The payload needs to receive GPS signals. Verification: Test - A test will be run to ensure that the antenna can take the measurements needed. DR : The operational payload shall have a receiver to read the RFI measurements. Motivation: The payload needs to be capable of processing the received GPS signal. Verification: Inspection - The receiver will be visually inspected. DR 11.5: The operational payload shall have a transmitter to relay the data, in real time, back to the ground station. Motivation: The payload needs to broadcast data for the user. Verification: Inspection - The transmitter will be shown to the end user in the operational payload. DR 11.6: The operational payload shall have an exterior interface for power and data post processing. Motivation: The payload is self contained so it will need to be accessed for power recharging and data post processing once on the ground. Verification: Inspection and Demonstration - A demonstration shall show that the data and power can be transferred using these interfaces. DR 11.7: The operational payload shall have an access panel on the exterior structure. Motivation: There needs to be a way to access the electronics inside the payload if an error occurs or a component needs to be replaced. Verification: Demonstration - A demonstration will show how to access the electronics inside UAS Selection 5. Key Design Options Considered The UAS platform can utilize different lift methods to achieve flight. A multi-rotor, fixed wing, and blimp were all considered as candidates for the flight platform. The following sections highlight each platform and explain both their strengths and weaknesses. Monday 2 nd October, of 38

16 Multi-Rotor Figure 3. 4 Rotor UAS Design The multi-rotor system achieves lift by facing one or more propellers in the vertical direction and generates thrust upward to support itself in the air. The most common configuration of a rotor for commercial use is a quadcopter, with 4 propellers in a square configuration. Additional types of copters include hexacopters and octocopters with 6 and 8 propellers respectively. These multi-rotors maneuver by adjusting the speed of individual propellers or by physically tilting the propellers. Some advantages to a multi-rotor are that they are stable at slow speeds, capable of hovering, take off and land in small areas, and can quickly maneuverer. However, because these rotors achieve lift only through propellers, they are inefficient, have a low payload capacity, and require complex flight systems. They are also relatively expensive given their complexity. The following table outlines the pros and cons of rotor type UAS systems. Pros Stable in slow fight Simple Take off and Landing Agile Table 3. Rotor UAS Pros and Cons Cons Low Payload Capacity Short flight times Complex control systems Does not simulate operational payload environment Poor performance in adverse weather Expensive Monday 2 nd October, of 38

17 Fixed wing Figure 4. Fixed Wing UAS Design The fixed wing UAS operates similar to a traditional airplane. It produces lift using an airfoil, maneuvers using smaller control surfaces which direct airflow, and produces thrust using one or more propellers. The fixed wing can come in multiple configurations including conventional, delta wing, dual boom, v-tail, and many more. The fixed wing is capable of long flight times and high payload capacities at a much lower cost than that of multi-rotors or blimps. In addition, they are relatively simple to program using flight controllers and work well in adverse weather conditions. The downside to this platform is that they cannot be launched or landed in as small of an area as that of a blimp or multi-rotor, certain aircraft suffer from poor flight characteristics, they cannot hover or maneuver as quickly as a multirotor, and they have a steep learning curve for manual flight modes. The following pros and cons table summarizes the characteristics described here. Table 4. Fixed wing UAS Pros and Cons Pros High payload capacity Long flight times High flight speed Simple Autonomous Control Good performance in adverse weather Affordable Simulates operational payload environment Cons Large take off and landing area No hover or slower flight capabilities Steep learning curve for manual flight Monday 2 nd October, of 38

18 Blimp Figure 5. Blimp UAS Configuration The blimp system operates using a vessel filled with a low density gas in order to achieve neutral buoyancy at a specific altitude. Propellers mounted to a gimbaled system are used to maneuver the balloon. Blimps have long endurances, moderate payload capacities, and are relatively simple to operate. However, autopilot systems are not well supported, the blimp itself is very expensive, and their top speeds will not reach beyond the minimum wind speed requirement for platforms within the budget. The following table displays the pros and cons mentioned above. Pros Moderate payload capacity Small Take off and Landing areas Hover Capabilities Table 5. Blimp UAS Pros and Cons Cons Poor Adverse Weather Performance Requires Helium Larger storage areas needed Slow to deploy and tear down Higher Maintenance Summary All options discussed above could be given further consideration for platform selection, however, due to the fact that the customer will be integrating the operational payload on a military grade fixed wing aircraft, the rotor and balloon systems were not chosen. The customer has specifically requested that the flight platform be a fixed wing UAS in order to closely simulate the operational payload environment (DR 1.1). The UAS is required to be compatible with an autopilot system, capable of flying for at least 60 minutes, and capable of flying in wind conditions up to 40 km/hr by FR 4, 1, and 2, respectively. As a result, only platforms which were capable of meeting this criteria were considered, and lead to the decision that a fixed wing UAS will best accomplish the goals of the project. With a fixed-wing UAS as the platform the team plans to pursue, the next step is choosing a specific model. For that decision, options such as cost, payload capacity, airframe complexity, and support by CU s IRISS team will be considered in a trade study, ensuring a model that best fits the project s needs will be met. This step will be completed for Preliminary Design Review (PDR). Monday 2 nd October, of 38

19 5.2. Autopilot Software The autopilot selection is crucial for the assessment of the capabilities of the aircraft. The autopilot will be used to autonomously fly the UAS based on pre-programmed instructions and waypoints. The three main design options for autopilot software will be using the software already installed on an off-the shelf UAS, open-source software on an airframe, or developing an autopilot from the ground-up. Ultimately, the choice of UAS will have an impact on the autopilot used. If an off-the-shelf UAS is chosen, the built-in autopilot will likely be used. Based on FR 6, the autopilot must be capable of autonomous flight, with no inputs from the ground controller. The selected autopilot must also adhere to DR and 6.1.2, allowing the UAS to remain in steady, level flight, even in the presence of wind. Due to the software complexity of FR 7, well-documented open-source software would give an advantage with editing the autopilot code. This would allow the team more freedom with augmenting the software for switching between GPS-aided and GPS-denied flight, as opposed to working on a proprietary black box system. In addition, the autopilot must be able to save data and interface with additional inertial sensors, in accordance with DR Proprietary Autopilot Most off-the-shelf UASs come with their own proprietary autopilot software. For example, the customer has performed preliminary tests on the Parrot Disco, which sports Parrot s C.H.U.C.K. autopilot system. The main issue the customer has run into while using this autopilot is that due to its black-box nature, it is difficult to work around and modify the onboard software. Additionally, proprietary autopilots are available for use on other airframes, such as MicroPilot. MicroPilot, along with several other proprietary solutions, carry suites of software that an open-source solution does not, however the cost may be prohibitive. The lowest-grade MicroPilot autopilot costs $1500, potentially putting the team over budget. Table 6. Proprietary Autopilot Pros and Cons Pros Cons Extensively tested and reliable Difficult to work around black-box Support available from manufacturer Costly Open-Source Autopilot Generally, when building a UAS up from the airframe, an open-source autopilot will be utilized. There are many options, with a variety of applications and functionality. The main advantage over using a proprietary autopilot is the modifiability of the software. If changes are required, the autopilot software can be edited to suit the needs of the UAS. Good examples of widely-used open-source autopilots are PX4 and ArduPilot. Both systems operate on a PixHawk microcontroller, allowing for extensive connectivity to external sensors and telemetry equipment. Running ArduPilot on a PixHawk microcontroller would be a feasible option, as the team would be able to use the CU Boulder IRISS team s expertise. Cory Dixon, as well as the rest of the IRISS team, has extensive experience with this autopilot system. Table 7. Open-Source Autopilot Pros and Cons Pros Highly modifiable Community support Free Cons Need to interface with UAS Ground-Up Autopilot The final option for autopilot software is building an autopilot from the ground up. This would allow the most flexibility, as the team can create the software exactly to the needs and requirements of the project. However, the difficulty of this method is prohibitive, and out of the scope of this project. Monday 2 nd October, of 38

20 Table 8. Ground-Up Autopilot Pros and Cons Pros Cons Most flexibility in design options Extremely difficult Free Limited support 5.3. Localization Method One of the key requirements of this project is the localization of an RFI source. Functional Requirement 9 states that the UAS must localize the RFI source within a certain area, and generate a power profile of the search area. The UAS and payload hardware will require a software interface in order to localize the source and generate the RF profile. This localization algorithm must perform in GPS denied conditions, so several ranging techniques were studied and compared to gauge which method would be optimal for this project. These methods are outlined in the following sections Time Difference of Arrival Method Time Difference of Arrival (TDOA) is a ranging method that relies on time delays between signals to determine location. This method of localization is utilized by the GPS system and relies on signals arriving at different times with the same velocity. The method can also utilize signals with different known velocities, for example a radio signal accompanied by an acoustic signal. This method can be extremely accurate for parameters such as signal velocity (in the case of an acoustic signal), and the synchronization of the dual signal transmissions. The distance from the sensor to the signal is shown in equation 1. The pros and cons of this method are shown in table 9. d = (v 1 v 2 )(t 2 t 1 t delay ) (1) Figure shows three different methods of ranging using TDOA. Figure (a) shows a single-signal time measurement which relies on precise clock synchronization between the sender and receiver. Figure (b) shows a single-signal two-way time measurement which uses the same precise clock synchronization as the single-signal technique. The two-way method can be slightly more accurate, especially with a moving target. Figure (c) shows the third TDOA technique. This is the only technique that does not require clock synchronization between the sender and receiver. However, this method does require that the source emits two signals at different velocities. Figure 6. TDOA Ranging Techniques Pros No time synchronization necessary Does not require GPS Relatively simple equations for algorithm Table 9. Pros and Cons: TDOA Cons Requires two separate signals from source Only provides distance to source The main drawback of this method is the assumptions that must be made about the signal source. This method will not be feasible unless the source emits two signals with different velocities, or the clocks on the sender and receiver are precisely synchronized. This method would most likely require additional hardware and may pose design problems for integration with payload hardware. Monday 2 nd October, of 38

21 Angle of Arrival Method Angle Difference of Arrival (AoA) utilizes the angle of arrival of a signal relative to a reference angle. This ranging method typically requires an antenna array. The two main techniques for AoA ranging are phase interferometry and beamforming. Phase interferometry utilizes TDOA measurements with individual elements of the array, and uses phase differences of arriving signals to determine a distance and direction to a signal source. This method requires signal/receiver clock synchronization or two signal sources at different angles for localization. Beamforming calculates the angle of arrival by moving the array around the search area. This method can be an extremely accurate ranging technique with the correct equipment and algorithmic solution; however, the necessary equipment can often be expensive and difficult to set up properly. The pros and cons for this method are shown in table 10. Figure depicts the general functionality of antenna arrays utilizing phase interferometry. Figure 7. AoA Technique Table 10. Pros and Cons: AoA Pros No time synchronization necessary Does not require GPS Can be extremely accurate Cons Requires antenna array Only provides distance to source Difficult algorithm Requires use of TDOA Power Difference of Arrival Method Power Difference of Arrival (PDOA) is another popular ranging technique that relies on the power-decay of a signal with distance from source. This method is very simple, and does not require extra hardware. The main drawback to this method is that noise interference can significantly reduce localization accuracy. This method would not be preferable in a location with many interfering signals. PDOA can be determined using equation 2. The pros and cons for this method are shown in table 11. Figure 8. PDOA Using Free Path Loss P r λ 2 = G t G r (2) P t (4π) 2 R 2 Monday 2 nd October, of 38

22 Pros No time synchronization necessary Does not require GPS No extra hardware required Simple algorithm Table 11. Pros and Cons: PDOA Cons Can be affected by noise Slightly lower localization accuracy Triangulation Triangulation is a straightforward method of localization using at least three stationary ground sensors. The three sensors localize by reducing the two-dimensional area in which a source could be by overlapping signals. This is shown in figure The localization can be extended into three dimensions by adding a fourth station at some height about the other three stations. This is another method used by the GPS network. This method can include expensive hardware due to the number of ground stations. This algorithmic solution is also fairly complex, especially when triangulation is done in three dimensions. Additionally, this method requires some knowledge of the general search area ahead of time. The pros and cons for this method are shown in table 12. Pros Can be extremely accurate Does not require GPS Table 12. Pros and Cons: Triangulation Cons Requires stationary ground sensors Complicated algorithmic solution Extensive hardware requirements Cannot be integrated into a single payload Relative search area must be known 5.4. Methods of GPS Denied Guidance Dead Reckoning with Inertial Navigation System Dead reckoning is a navigation method which historically has been used for maritime navigation, and recently adapted for aeronautical navigation. At the highest level, dead reckoning is the process of estimating a vehicle s current position relative to its last known position. To do this, a heading and speed measurements are integrated with time and the new position is estimated with respect to the vehicle s initial position. For aeronautical applications, namely UAS navigation, an Inertial Navigation System (INS) can be used to determine the current state of the aircraft based on inertial measurements (linear and angular accelerations in inertial space). The most basic INS consists of an Inertial Measurement Unit (IMU), which is a triad of gyroscopes and a triad of accelerometers aligned with the aircraft s body frame. a Single integration of the gyroscope data give roll, pitch, and yaw angles of the UAS. Single integration of the accelerometer data gives the aircraft s linear velocities, and double integration of the accelerometer data gives a a Alignment to the aircraft s inertial frame assumes implementation of the strap-down configuration, where the INS is strapped-down to the aircraft, such that it moves with the aircraft. The INS can also be mounted on a stabilized platform which is a gimbaled system to allow rotations independent of the aircraft s motion. The stabilized platform configuration keeps the INS aligned to the North, East, Down frame, but requires extremely heavy setup structures and is susceptible to failures such as gimbal lock. Due to the potential of gimbal lock, as well as the large size and mass, the stabilized platform configuration was not considered for RAMROD s UAS. Monday 2 nd October, of 38

23 position estimate relative to the previous location. Figures 9 and 10 show a basic example of two dimensional dead reckoning and a basic block diagram of the INS function, respectively. Figure 10. Basic block diagram of INS. b Figure 9. Basic depiction of dead reckoning with arbitrary values. One of the most advantageous characteristics of an INS is its ability to incorporate additional state determination sensors into its design, such as magnetometers, altimeters, and airspeed sensors (such as pitot tubes). By adding these supplemental sensors, the INS can use actual measurements in its calculations where estimations were required previously. Additionally, these sensors are typically small, easy to integrate with each other, and can be very precise. Each of these sensors, however are susceptible to predisposed errors, namely accelerometer bias and gyro angle random walk. These errors tend to propagate over extended periods of time, but use of higher grade sensors and recalibrating when able can help reduce these errors. Increased precision comes at a cost, however. Navigation grade sensors can range upwards of a couple thousand dollars but are incredibly precise, while consumer grade sensors are very affordable but have very large error margins. Additionally, the INS requires some sort of filter to aid the fusion of data, which in most cases is a Kalman or Extended Kalman Filter. Table 13 provides a summary of the discussed advantages and disadvantages, as well as additional pros and cons of using a dead reckoning method with INS. 8 Table 13. Pros and Cons: Dead Reckoning with INS Pros Cons Small in size Expensive for high grade precision Light weight Susceptible to accelerometer bias error Can be inexpensive Susceptible to misalignment errors Supports aiding from other sensors Susceptible to gyro angle random walk Fast calculation rates Requires filter for fusing data Low power consumption LiDAR Based Localization and Mapping Light detection and ranging (LiDAR) devices are active remote sensing devices, which have a multitude of applications. Recently, LiDAR technology is proliferating the autonomous navigation field of research, especially those areas focused on UAS navigation and obstacle avoidance. LiDARs function similarly to radars, but instead of transmitting microwave signals, LiDARs transmit pulsed optical, or light signals (sometimes referred to as Light Radar ). The signals are typically in the visible to near-infrared (IR) spectrum and transmitted in approximately 1µm wavelengths, which allows the LiDAR system to measure small objects with very high accuracy. The pulsed signal reflects off of objects and returns to the device. The LiDAR then calculates the returns of each pulse, ultimately determining range and rendering a two or three dimensional map of the scanned area. Figure 11 shows an illustration of this process. 9 b Adapted from K. Grade 8 Monday 2 nd October, of 38

24 Figure 11. Basic operation of an aircraft mounted LiDAR. 1 Due to their fast data rates and impeccable accuracy, LiDARs are great for applications such as object detection and avoidance, indoor flight using small UAS, flight in urban canyon environments, and navigation in GPS denied environments. The latter, of particular interest to the RAMROD project, can be achieved by way of a simultaneous localization and mapping (SLAM) method. Using this method, the UAS would follow a flight path while mapping the terrain and comparing to known data of the area. One major, and obvious, limitation of this technique is that it requires some known information of the area, or some sort of positional assistance. Other major limitations include the size and range of LiDAR devices. Long range devices are typically very large in size with respect to a UAS. These advantages and limitations, as well as others, are summarized in Table Table 14. Pros and cons of LiDAR for GPS-denied navigation. Pros Capable of re-creating highly accurate 2D or 3D maps Allows for obstacle avoidance Not susceptible to predisposed precision errors Can detect range Useful for landmark detection and identification Very high data rates Cons High power consumption Typically very expensive for long range devices Large in size and weight Requires large amounts of processing power Can fail to achieve location matching in environment with few orienting structures Requires some reference state or location LTE Localization Localization by way of a cellular network is a well established technique, used for many applications such as emergency services locating 911 calls. Localization occurs by transmitting data between base transceiver stations (BTS, or enb for LTE) and user equipment (UE) over a secured user plane. These localization protocol signals can be initiated by either the network or the UE. In the case of UE initiated location services, there is a Location Service Client server (LS) built into the network architecture, which processes the requested data from the client (UE) and provides the client with position information. The two main techniques used for locating user equipment (UE) in a network are observed time difference of arrival (OTDOA), and enhanced cell ID (ecid). OTDOA position estimates are made by measuring the TDOA of signals from two enbs, where each measurement describes a hyperbola with focus points at the enbs. TDOA measurements must be made with three pairs of enbs in order to determine location where the Monday 2 nd October, of 38

25 11, 12 hyperbolas cross, which is where the UE is located. This technique is illustrated in Fig. 12. Figure 12. Illustration of OTDOA using hyperbolas. 12 The ecid technique uses knowledge of the location of the base station, combined with making measurements of radio signals. These can be either triangulation or angle of arrival measurements, which are conceptually the same as the triangulation and AoA methods described in Section 5.3. Further illustrative descriptions can be seen in Figs. 13 & Figure 13. ecid Triangulation 12 Figure 14. ecid Angle of Arrival 12 LTE localization methods are potentially very useful when utilized in complex lower airspace (<1000 ft), where GPS signals can be blocked or deflected by various obstacles. The key advantages of using LTE/cellular localization methods are that they operate at relatively fast data rates, allowing for real time position updates, and it is a technique which is already proven to work, with protocols defined and continually updated by the Open Mobile Alliance. The major limitation of this method is that it often can have position errors on the order of m when used alone. Accuracy can, however, be improved with the introduction of an integrated system. Table 15 has a complete summary of the pros and cons of the LTE localization method. 11 Monday 2 nd October, of 38