Welcome. David Leiss Senior Application Engineer Agilent EEsof EDA. Dingqing Lu Senior Application Engineer Agilent EEsof EDA

Transcription

1 Welcome David Leiss Senior Application Engineer Agilent EEsof EDA Dingqing Lu Senior Application Engineer Agilent EEsof EDA

2 Agenda Radar EW System Simulation and Test Concepts System Development Challenges in Today s World A New Test Platform Methodology Radar Key Models and Supported Systems Electronic Warfare Key Models and Supported Systems The Integration of Design and Test for Radar & EW Putting it All Together Virtual Tactical Scenarios Summary 2

3 Radar EW Challenges Transmitter Interference Target Radar Receiver Clutter Jamming deception Target with EW EW EW How can I reduce the time and cost to develop the next generation of Radar and EW systems? The two biggest costs are often testing and validation, especially flight testing. What can I do to reduce the these expenses? Radar and EW systems are getting more and more complex, how do validate performance earlier/continuously, instead of waiting until final integration & test? How do I get all the legacy Intellectual Property (IP) point tools to work together with RF to insure success with this project, and in the future? 3

4 Proposed Platform for Simulation Signal generation Transmitter Interference EW Radar DSP & RF Models Duplexer Antenna Target environments RCS, Clutter, RF receiver Detection Rx measurements Signal processor Receiver down/conv Clutter Jamming Waveform gen Signal proc Model-based system platform encompasses both RF & BB development Continuous validation of system performance and test processes Integrate existing IP into the development process Add custom models using MATLAB, C++, HDL, etc. Integrate existing hardware into design using test equipment links Perform high quality RF co-simulation using Spectrasys and ADS/GG External simulation control from other tools, such as C#, LabVIEW, MATLAB Validate performance using virtual flight testing/terrain, using STK from AGI 4

5 Proposed Platform using Virtual Scenarios & EW External control of SystemVue allows creative RF/BB modeling SystemVue Radar signal path models 3 rd party software (STK by AGI) STK feeds path, Doppler, terrain/clutter info to SystemVue for RF rendering Links to T&M equipment Allows direct validation and rendering of algorithms and scenarios 5

6 Agilent Aerospace & Defense Solutions Focus where it counts Radar EW Key Models and Supported Systems 6

7 Source Models Pulse signal generator Linear FM pulse waveform (LFM) Nonlinear FM waveform (NLFM) Binary phase coded waveform (Barker) Poly phase coded waveform (Frank, P- Code, ZC) Continue Waveform (CW) FMCW Ultra Wideband (UWB) Step Frequency Source SAR Source freq_out_ RADAR_CW waveform_out_ R1 Models} Waveform_type=Sawtooth Amplitude=1V Period=1s Lower_freq=10Hz Delta_f=50Hz Sample_rate=200Hz I LFM FMCW NLFM Barker Frank FMCW Signals: A. Waveforms B. Frequency vs Time UWB UWB Signal: Waveform and Spectrum 7

8 Direct Digital Synthesis (DDS) Model CW, Pulse, LFM, Stepped Frequency Pulse and LFM 8

9 Target Model More Environment parameters supported Earth effect Atmospheric Loss More RCS Types System_Loss Ground Reflection Polarization Dielectric effects Trajectory 9

10 Clutter modeling Use statistical approach to model clutter; combination of Probability density functions (PDF) for clutter amplitude Rayleigh Log-Normal Weibull K Clutter Clutter power spectrum density (PSD) Gaussian Cauchy All Pole Waveform Histogram Detected Signal in K Clutter Simulate sea clutter with K-Clutter model 10

11 Transmitter (model hierarchy) Customizable Tx sub-network Digital up-converter (DUC) Phase noise DUC Filter synthesis Nonlinearities 11

12 Receiver (model hierarchy) Digital downconverter (DDC) Customizable RX sub-network RF gain w/ noise figure BPF DDC 12

13 Search and Tracking Radar Modeling 1. Wavegate model: The wavegate refers to a fraction of time window in one PRI for efficient simulation. 2. Pulse Compression model: It is implemented in Frequency domain with a pulse compression algorithm which is widely used in the radar system. 3. Radar Detector: This model is used to detect the target in the noise environment based on improved Bernoulli algorithm. 4. Target Tracking: This model is used to track the target and measure the target range when the target is detected. Demo 13

14 Antenna modeling, at the system level Supports key parameters for the following Tracking and searching modes Different scan modes Moving target scenarios 14

15 Genuine RF transceiver chain (additional modeling fidelity) X-parameter model From Measurement, or Simulation Challenge How to account for accurate RF at the system-level RF System Architecture Using dedicated RF simulator Drag & Drop to the Dataflow System-level Dataflow modeling on the fly 15

16 System-to-Circuit direct co-simulation SystemVue ADS For more technical information, please visit the ESL Application Center article at and read Agilent app note EN. 16

17 Radar Measurements Basic measurements Waveform Spectrum Signal Noise Ratio Advanced measurements Estimation of Distances and Speed Detection probability False Alarm probability Antenna Pattern Measurements False Alarm Rate Prob. Detection vs. S/N Transmission signal Received target return signal Target return signal with clutter 17 17

18 Design Case 1: Pulsed Doppler System Challenge How to detect targets in complex environments Moving Target Environment: very low SNR or SCR Pulse Doppler System Tx RX measurements required FFT Size = 2 FFT Size = 32 FFT Size = 128 Figure: Probability of detection vs range 18

19 Design Case 2: Stepped-Frequency Radar (SFR) Frequency fo fo fo fo fo Frequency Conventional Pulsed Doppler Radar Stepped- Frequency Radar Time Challenges Higher Resolution Lower Cost Design Choices: τ Δ f f 0 f 1 f 2 Tp NT f N - 1 f N - 2 f 0 f 1 Time Two targets (range=10 meters) 1. Regular Pulse Radar Resolution - Rs: Assuming T = 0.25 us, fo = 1/T, Rs = C/(2*fo) = 37.5 m If you want Rs = 0.58, then T = 3.9 ns, SFR (2 detected) 2. Step Frequency Radar: N = 64 With Freq Hopping, Time Division Rs= C/(2N*fo) = 0.58 m Rs Higher Cost relatively lower and SCR Higher x Pulsed (1 failed detect) 19

20 Design Case 3: Synthetic Aperture Radar (SAR) Synthetic aperture radar (SAR) is one of the most important high resolution imaging radar. SAR offers dramatically improved image resolution over radar without sophisticated post processing by utilizing the movement of the antenna with respect to the target. Challenge: Higher resolution imaging SAR echo generator RADAR_SAR_Echo R1 Models} SAR_Mode=Stripmap SlantRange_ZeroDopplerPlane=7500M Radar_Velocity=200 [Vr] Antenna_Aperture=1M [La] Pulse_Width=6.033e-6s [Tr] LFM_Rate=4e+12 [Kr] Carrier_Frequency=10e+9Hz Squint_Angle=0 [theta_sq_c] Range_SamplingRate=30e+6Hz PRF=600Hz [Fa] Duration=1.5s [Duration] HalfTargetAreaWidth=200M EchoGenerate_Mode=Point_Target TargetInfo=(1x15) [0,0,2,0,-0.3,1,0,0.3 Simulation of a Synthetic Aperture Radar system in X band with 10 GHz of Center Frequency, MHz of Bandwidth and msec of RPI. 20

21 Design Case 4: Phased Array Radar Passive Antenna System (PAS) Active Antenna System (AAS) T/R T/R T/R T/R T/R T/R Beamformer Beamformer BB source BB receiver Tx Rx LNA Filter X Filter A/D BB source BB receiver T/R PA Filter ~ ~ X Filter D/A Array Antenna driven by single large TX HPA First receive LNA after beam is formed Large signal loss between radiating element and transmitter/lna Antenna connects to transmitter and receiver 21 T/R Module behind each radiating element Transmitter distributes through many small HPAs to Antennas First receiver distributed through antenna in many small LNAs Small signal losses between HPA/LNA and radiating element

22 Modern Phased Array Radar Challenges Problems T/R modules and DDS are very expensive for high-resolution DAC /ADC T/R module calibration is very difficult Field test is very expensive provide emulation environments How do I monitor Array and sub- Array abatement properly for better performance? Can we manage the R&D lifecycle better? How Agilent Tools Add Value Trade-off Performance vs Cost Adaptive algorithm to fix amplitude /phase errors Provide emulation environments that account for o Clutter o Targets o Interference o STK link Validate performance vs. spec based on Measured Antenna Patterns TX measurements such as Waveform, Spectrum, Constellation and EVM RX measurements such as Detection Rate, False alarm rate Shorten the design cycle by Model-based simulation instead of function based Platform for Integrated solution 22

23 Beam pattern (db) Proposed Platform Solutions for AESA Circuits M9381A 0-50 DUT Signal generation AESA Radar Beamformer T/R module T/R module T/R module Antenna array Clutter Interference Target Ant pattern u y u x measuremen t Target with EW Jamming T/R module EM M9703 Rx measurements System with DDC2 Signal processor System with DDC1 Receiver down/conv Benefits from AESA prototyping platform Overcome test challenges for calibrations in active antennas Reduce product Cost by Trade- Off analysis 23

24 Phased Array Antenna Elements Z Y X 24

25 Key Model: Beamformer As an example, consider an Uniform Line Array Through signal processing as seen below, spatial filtering for interference can be archived. Propagation can form a response pattern with higher sensitivity in desired directions. Electronically scanned radars eliminate the mechanical challenges and errors associated with rotating and changing the elevation of a dish antenna. Array elements can create simultaneous beams to increase flexibility and capability s T (t) x 0 ( t) d 0 x ( t 1 ) w w 1 xi ( t) st ( t i ), i 1,2,... M d sin c ( M 1) x M (t) y( t) M 1 i 0 w x [ t ( M i i i 1) T ] w M 26

26 Beamforming Ex. 1: Phased Array Radar Simulation Magnitude and phase error correction in SystemVue Challenges Calibration is difficult Phase error correction The red line is the antenna pattern with magnitude and phase error The blue line is the antenna pattern after error correction 27

27 Beamforming Ex. 2: Digital Phased Array Template for component design, such as DDC, DUC, DAC, ADC, PA Key DAR components, working in complex environment, need to be modeled, simulated and evaluated to create proof-of-concept results for novel radar architectures. T/R modules constructing with different DDC, ADC, PA, System with phase/amplitude noise can be designed and evaluated using the template. DDC in the T/R Module Challenges Trade off analysis To reduce cost Compare performance with two digital downconverter (DDC) algorithms DDC2 Digital T/R Module DDC1 28

28 Beamforming Ex. 3: Two Sub-Array System Link-level system simulation for concept demo using Radar reference library, includes: Source, Beamformer, T/R, Antennas, Ant Measurements Targets, Interference, clutter, Receiver, signal processing Monte Carlo Can be added without technical difficulty Link to Circuits and EM for more detailed performance Arbitrary antenna layout is under investigation Challenges Multi Target Detection Phased Array Beam pattern (db) u u y x Beam pattern (db) u y u x

29 Design Case 6: SV Workspace for FMCW Radar.m model 1. FMCW Radar simulation template 2. Transmitter signal can be generated. Radar target return signals with RCS, Clutters, Jamming and interferences also can be generated 3. Signal processing subnet model is created to processing the FMCW signal 4. Rage Estimation algorithm is created by using.m model. Easy to use 5. Simulation results show the algorithm working properly. Challenges Detection algorithm for complex environments 30

30 Full Transmit/Receive Test Instrument Setup Alt) M9330A + PSG configuration M8190A-AWG 24,77,79 GHz Up-Converter Custom Design PCIe DUT Control Interface Waveform Creation Instrument Control N5183A-520 Local Oscillator DUT SystemVue w/radar Library LAN Oscilloscope 24,77,79 GHz Down-Converter Custom Design 31

31 Agilent Aerospace & Defense Solutions Focus where it counts EW Key Models & Supported Systems 32

32 Electronic Warfare (EW) Concept Transmitter Interference Target Target with EW Radar Receiver Clutter Jamming or deception EW Electronic Warfare (EW) It is military action using electromagnetic and directed energy to control the electromagnetic spectrum or to attack the enemy. EW includes o o o Electronic attack (EA or ECM) Electronic protection (EP) Electronic warfare support (ES) 33

33 Electronic Attack (EA) based on DRFM Design and validation of a ECM algorithm. example: Digital Radio Frequency Memory (DRFM) An LFM reference signal is generated. The ECM system detects it, modifies it, and re-transmits at high amplitude. Challenge False Target deception Original Signal ECM Signal 34

34 Electronic Protection (EP) Receiver Direction of Arrival (DOA) Estimation EW receivers estimate the DOA of incoming signals Two Algorithms: MUSIC and ESPRIT Challenge DOA under Complex Environments 35

35 Electronic Support (ES) Signal Generation: testing RWR RWR may play a critical role in a one on-one engagement, informing the aircrew of the threat, its operating mode, and bearing. 36

36 Agilent Aerospace & Defense Solutions Focus where it counts Radar EW Integrating Design & Test 37

37 Radar EW Test Platform Signal generation Radar Transmitter Duplexer Antenna Interference Target environments RCS, Clutter, RF receiver EW Detection Rx measurement s Signal processor Receiver down/conv Waveform gen Signal proc VSA//scope DUT VSA/ Scope DUT AWG ARB UWB ARB0 SystemVue / M8190A/M VSA Wideband PSG M9381A DUT Infiniium scope /M9392A /M

38 Basic Waveform Generation - Target Return Signals Radar Models SystemVue DOWNLOAD FROM SystemVue Clutter, noise, and Interference BB Pattern Generator BB AWG RF Signal Generator LFM Transmission Signal Received Target Return Signal Target Return Signal with Clutter

39 Advanced Measurements Receiver Test Signal Processor HW MXA/PXA, VSA LA, Scope ESG/PSG/MXG/PXB PD vs. S/N PD Probability of detection 40

40 Generating Test Signals Test signals Radar transmitter Standard waveforms Radar receiver Target emulation: Target Returned with RCS Plus Clutter, Interference and Jamming EW EW emulation: Transmission Waveforms Plus Interference Transmitter Interference Target Receiver Clutter Jamming deception Target with EW EW Single mode waveforms (generate directly using simulation models) Pulse Pulsed Doppler SFR SAR, DAR, FMCW, UWB, Multi mode waveforms (assemble using subnetworks, scripts) Signal combiner Time framing Channelization Waveform sequence composer 41

41 Waveform Sequence Composer What is it? Easy, new utility included in SystemVue direct download to instruments Or, use as a simulation source Creates complex signals with efficient waveform memory usage Excellent companion to AWGs, VSGs W2 {WaveformSequencerEnv@Data Flow Models} WaveformSequenceComposer='Frequency Hop Periodic=YES Why use it? Segments with repetitive signals: download them once, repeat using commands Save waveform memory : repeat idle signal intervals as segments M9381A VSG Key applications Receiver test for Radar, EW, MilCom, SatCom Frequency agility (quickly alternate between Segments to imitate frequency-hopping) M8190A AWG 42

42 Waveform Sequence Composer example Collect datasets & files with pre-calculated waveform data ( segments ) Assemble segments into a composite waveform Then, download! 43

43 EW Signals Analyzed by VSA Spec A Spec B Clutter Spec C Wave A Wave B Wave C Wave A Wave B 44

44 Electronic Counter-Measures (Digital RF Memory) ECM with DRFM Challenges Platform for implementing ECM 45

45 Agilent Aerospace & Defense Solutions Focus where it counts Putting it all Together: Virtual Scenarios 46

46 SystemVue & STK for Virtual Scenarios Transmitter Receiver Virtual testing for a fighter as a target. It starts at 10,000 ft and is detected by the radar so it dives down to do low-level terrain following to try to get below the radar, sometimes successfully, sometimes not. This allows the user to do Virtual Flight Testing of existing and proposed Radar systems with inclusion of the DSP processing and real-world RF chain impairments along with the aircraft repeatedly going through its paces. 47

47 End-to-End Radar System Simulation (w/terrain clutter) TERRAIN DATABASE Cx Chirp Transmitter LO Tx Out 48

48 LO Phase Noise Sweep: SystemVue with STK P3_TankDetection13a.wsv In this case, how does radar LO Phase Noise level effect Probability of Detection? Note: Any parameter could have been swept, such as clutter, Tx Pwr, jammer power, etc. 49

49 Using 3DEM-based RCS predictions in System-Level Performance Radar Cross Section Aircraft2 50

50 Using 3DEM-based RCS predictions in System-Level Performance EMPro_FullPattern_RCS2.rcs 51

51 Integration of 3D RCS with SystemVue & STK Predicted 3D RCS results from 3DEM (EMPro) are included into an STK flight scenario, with terrain/clutter A SystemVue simulation is then performed of the entire system. In certain aircraft positions, the RCS is reduced to the point where a complete loss of detection occurs. Probability of Detection with 100-sample moving average 52

52 Summary In this presentation, a model-based system approach is proposed: For radar systems, ideal radar transmission signals and also radar received signals with environments can be created for testing. For EW system, not only EW received signals can be generated to test EW receiver, but also EW transmitted signals, either a jamming or deception signals can be created to test radar receivers. Model-based environment allows Design & simulation to be leveraged for Test for lower NRE, easier data exchange, and cross-domain DSP/RF collaboration. Reference IP: Simulation templates and modifiable reference designs are flexible, convenient, and integrate easily with algorithm tools and ultrawideband test equipment for realistic performance evaluation Virtual scenarios can include RF, EM, kinetic/inertial modeling, as well as traditional baseband DSP and sensors/converters. By emulating expensive field environments, many design problems can be solved less inexpensively in R&D. 53

53 For Additional Information: Product Web sites: SystemVue: Aerospace & Defense: Radar: At the sites above, select Contact an Expert for further information. or Contact you local Agilent EEsof sales representative. Contact information: 54

54 You are invited Dr. Ilcho Angelov Associate Professor Microwave Electronics Lab Department of Microtechnology and Nanoscience Chalmers University Goteborg Sweden You can find more webcasts Roberto Tinti Device Modeling Product Planner Agilent EEsof EDA

55 References 1. Dingqing Lu and Zhengrong Zhou, Integrated Solutions for testing Wireless Communication Systems, accepted by IEEE Com Mag, I. Skolnik, Radar Handbook, 2nd ed. McGraw-Hill, Inc D. Curtis Schleher, MTI and Pulse Doppler Radar, Artech House, Inc Dingqing Lu and Kong Yao "Importance Sampling Simulation Techniques Applied to Estimating False Alarm Probabilities," Proc. IEEE ISCAS, 1989, pp Dingqing Lu, Quasi-Analytical Method For Estimating low False Alarm Rate, EuRAD2010, 16-2, Sept, Dingqing Lu, "Simulation, test of stepped frequency radar systems, EE Times, Dingqing Lu, "Testing Radar EW Systems for the Real-World", MW Journal,

56 Additional Resources 1. EMG general app note 2. SystemVue Radar app notes SystemVue Radar product information W1905 Web page: W1905 datasheet: 4. YouTube Video: 5. Webcast: Uncovering the Hidden Impairments in Testing Advanced RADAR Systems 57