Reflections of the Future

Transcription

1 Reflections of the Future Trends in Radar Technology and their Impact on Test Presented by: Bob Cutler Agilent Technologies Technology Leadership Organization Copyright 2013 Agilent Technologies

2 Categorizing Radar Architectures Monostatic Bistatic MultiStatic (Netted) Angle / Time / Freq (Doppler) Space-Time Adaptive (STAP) MIMO Adaptive/Cognitive Imaging / Non-Imaging Synthetic Aperture (SAR/ISAR/CSAR) Synthetic Impulse and Aperture (SIAR) Active / Passive Multi-Mission: Multi-Function (MPAR or MFAR) Time Scheduled Freq Scheduled (OFDM) Non-Radar (e.g.comm s) Antenna Mech. Steered Passive Steered Array Electronic Steered Array Digital Array Digital Beam Forming Co-located Distributed Tube / GaAs / GaN / SiGe Photonics Active Signals CW / FMCW / Pulsed / Chirped Frequency Hopped Coded/Spread (e.g. Barker) Impulse / UWB OFDM Correlated / Uncorrelated Orthogonal Signals of Opportunity Broadcast AM/FM/TV Cellular Deployment Fixed Ground Airborne Land Mobile Naval Space Co-located Distributed Man Portable Application Surveillance: Air/Sea/Land/Space Air Traffic Control Fire Control Ground Moving Target (GMTI) Imaging / Mapping Navigation & Guidance (altimeters, terrain following, auto, autonomous ground vehicles, etc.) Weather Wall/Ground Penetrating Perimeter Security Law Enforcement Sports 2

3 PULSED RADAR Our Comfort Zone 3

4 Other Familiar Technologies FMCW / Doppler / Chirped / Barker FMCW: Frequency shift by delay (motion) Doppler: Frequency shift by motion only FREQ TX RX Target Motion POWER SPLITTER f TIME Delayed Return TX RX 4

5 RADAR TRENDS 1. Digital continues to move closer to the antenna Mechanically Steered > Electronically Steered Phased Array Passive ESA (PESA) > Active ESA (AESA) > Digital Array Radar (DAR) Steered Beams giving way to Digital Beam Forming (DBF) 2. Vacuum Electron Device (TUBES) giving way to Solid State. Higher performance (GaAs, GaN, SiC) Lower Cost (SiGe and even CMOS at mmwave) MMIC, SoC, Radar-on-a-chip 3. Radar Engineers have Discovered Shannon Applying Information Theory to radar More sophisticated algorithms and signals Signals adapt to detected targets and conditions. 4. Frequencies, Bandwidths, Resolution are increasing Better Resolution More Bands / Shared spectrum / Simultaneous operation on multiple bands Smaller platforms (e.g. drones) 5

6 TRENDS in RADAR and Remote Sensing 4. Technology Sharing between Commercial and Government Sectors GaN for Cellular Base Stations CMOS and SiGe (f t > 100 GHz) for Radar CPU, GPU and FPGA technology 5. Architectures support multiple functions Search, track, fire control, Weather, Synthetic Aperture Communications Electronic Warfare (EW) 6. Spatially distributed radar systems are more common Elements of the radar system are at different locations (multistatic) MIMO (may be co-located or widely spaced) 7. Number of array elements is increasing Cost, size and power of each element decreasing (i.e. modules are cheap) Higher levels of integration Conformal installation rather than planar 6

7 How Many Elements PAVE PAWS - 31 Years Old 1,792 Passive Elements AESA Airborne (Active) Elements 7

8 SBX 8 th Wonder of the World RADOME: 103 FT HIGH ANTENNA DIAMETER: 72 FT 45,056 MODULES RADAR: X-BAND PHASED ARRAY 8 Confidentiality Label

9 9 th Wonder of the World? DARPA ISIS The OS is launched like a satellite; once aloft it never lands until the end of its 10+ year lifetime. Within ten days it autonomously deploys to any designated worldwide location. The OS can maintain its station year-round within latitude bounds of -37 South to +55 North. 9

10 Test Implications More modules to test Cost of test, test times Array calibration Cost of test: test facilities, test times New approaches to calibration and functional tests (What worked for 3k elements probably might not be cost effective at higher element counts, and probably won t scale to 300k) Accuracy and stability with longer test times. Improve Measurement Throughput Choose the right approach to test Parallelize Choose the right equipment Optimize the software: Fill memory, empty during dead-time. Optimize the test sequence: Fastest tests in the inner loops. 10

11 Trend: Frequencies and Bandwidths Increasing Drivers and Enablers: Smaller Platforms (UAV) s Resolution (e.g. SAR) Spectrum Availability More bandwidth available Less crowded Improved Semiconductor Technology (SiGe / GaN / CMOS) GHz signals with 1-2 GHz bandwidth are no longer esoteric Scopes have become an important tool for wideband RF signal analysis Wide bandwidth, multi-channel, Vector Signal Analysis 11

12 Consumer 60 GHz Technology RF ASIC with ceramic antenna array bonded directly on top of RFIC. 36 Element Array Baseband ASIC 12

13 Digital Moving Closer to the Antenna

14 One Driver for Change: Multi-Function Radar Multiplexing Approaches Time (sequence) Frequency (Band, OFDM) Space (subarray) Simultaneous UK AIRBORNE AESA RADAR RESEARCH, Dr. Stephen Moore, Radar Team Leader, Dstl, UK 14

15 Passive Electronically Steered Arrays (PESA) High Power TX Sub array Sub array θ θ θ θ θ θ θ θ One Source of RF Energy Klystron Single Point of Failure High Voltage Requirements TR Isolation Circulator Duplexer Switch Power Distribution RX Sub array θ θ θ θ Phase Shifters One Receiver High Dynamic Range Requirements Single Point of Failure 15

16 Active Electronically Steered Arrays (AESA) Low Power TX Sub array Sub array RX Sub array Brick Tile -Module Technologies Today and Possible Evolutions, Patrick Schuh, et. al EADS 16

17 More Functions May Require More Channels Low Low Power Power TX TX RX RX RX Sub array Sub array Sub array More Channels enable: Simultaneous operation on multiple frequencies or bands Different functions on different sub-arrays Different Signals on Different parts of the array Co-located MIMO Other advanced functions 17

18 The Path to Digital Array Radar (DAR) 1 % e.g. 20 Channels for 2000 elements 10 % e.g. 200 Channels for 2000 elements 100% Advances in affordable Digital Array Radar, Chris Tarran BSc CEng MIET Roke Manor Research Limited 18

19 Example: Purdue Prototype DAR System Digital Baseband IQ RF DAR enabled by high levels of integration and Moore s Law Ultimate in flexibility Software Defined Signals can be modified in real time to task and condition Signals have bandwidth Likely to have a superset of performance specifications relative to AESA Figure from Digital Array Radar Panel Development, William Chappell, Caleb Fulton. Purdue University 19

20 OFDM DAR (Concept) OFDM-based Digital Array Radar with Frequency Domain Mode Multiplexing John P. Stralka, Northrop Grumman 20

21 OFDM Signals Have Noise-Like Pk/Avg ratios Typically given as 10dB % Time Exceeds Level 0.02% 8dB 1.4% 6 db 8.6% 4 db 21% 2 db 38% 0dB above average of -2.3 dbm This is not an OFDM Signal. Q: What s this signal s Peak-to-Average Ratio? A: It depends! Band Limited Gaussian Noise 8.5 db Only exceed 8dB above average.02% of the time. However, we have observed peaks up to 8.5dB above average. So, how often can you get away with driving the amplifier into saturation? Agilent Technologies

22 Pulsed Sine vs Complex Modulated Signals Need wideband to characterize the pulse Can use narrowband swept to measure the spectrum. Spectrum of a periodic signal Can improve SNR of spectrum by using narrow RBW s Need accurate, wide-band digitizers to capture signal without loss of information. Cannot improve SNR of spectrum by narrowing RBW Spectrum of a signal without cyclostationary components 22

23 Trend: RF Shifting from Tubes to Solid State From GaAs to GaN GaN provides higher power levels, greater robustness Test impact: power supplies, drive levels, and loads (pads) SiGe/CMOS with Ft s >> 100 GHz Low Cost, High levels of integration Efficiency an issue Thermal Management Spectral splatter / Emissions Pre-distortion 23

24 Pre-Distortion Waveform Fidelity e.g. Chirp Linearity Pulse Shape Emissions Spectral splatter Efficiency Higher Power Added Efficiency (PAE) Module to Module Gain Match? Implementations Linear (equalization) or Non-Linear Adaptive (comm s) or Lookup table or Pre-computed Waveforms Many design automation and test tools exist today Simulation tools such as SystemVue and ADS Digital Predistortion Algorithms X-Parameter Measurements for characterizing and modeling non-linear devices (PNA-X) 24

25 TX/RX Signal Fidelity How will new radar signal fidelity/accuracy be defined? Measured? For comm s signals we use demodulator based error-vector magnitude (EVM) Impairments impacting radar signal fidelity Phase noise AM/PM Conversion Intermodulation Distortion Amplifier Gain/Phase Stability (thermal or power supply) Additive Noise Spurious Baseband IQ modulation Errors (e.g. gain/phase imbalance) Meas(t) Meas(t) Reference Waveform Generator Normalize and Align Stored or Measured Reference Waveform Ref(t) - + Ref(t) Err(t) RMS Digital Comm Signals Err(t) RMS Any Signal 25

26 DistortionSuite and/or Power Sweeps Vector Signal Analyzer Measure with Live Signals Network Analyzer Measure with Power-Swept Tones AM/AM, AM/PM, Power Statistics, EVM AM/AM, AM/PM, S and X Parameters 26

27 Impact of Digital Moving Closer to the Antenna Functionality becoming software defined New capabilities added to existing designs Upgrades to deployed equipment (no return to factory for test) May need to verify performance drivers, not specific implementations Signals are not fixed by design, and may not be fixed during operation (adapt to target and conditions) Change with function Pre-compensate based on channel (target, clutter, jamming, etc) Signals convey information 27

28 Impact of Digital Moving Closer to the Antenna No Analog S21 Measurements (for DAR) Signals may be amplitude modulated (linearity) Signals Have Bandwidth (flatness, spurious) DSP TTD vs Phase/Gain Shift (for DAR) Digital Plumbing (interconnects) More Channels to Test Performance Metrics (new plus some old) Calibrations Test Modes Test Points Test Methods DSP D/A CLK A/D S21 28

29 DISTRIBUTED RADAR Confidentiality Label 29

30 Radar Architectures Phased array radar Multistatic radar MIMO-radar (Multi Input Multi Output) Tx Tx a SP SP SP SP Tx / RX Rx SP/Rx Phased Array: Single Waveform Multistatic: One or more illuminators. Signal Processing at each RX MIMO: Receive different linear combinations of TX signals with Joint Processing 30

31 MIMO Concept s 0 h 00 r0... TX h 01 h 10 RX s 1 r 1... h 11 What s Received: R = HS [] r 0 = [ r 1 h 00 h 01 h 10 h 11 ][] s0 s 1 Solving for S (comm s): S = H -1 R Solving for H (radar): H = S -1 R

32 Difference between MIMO and Phased Array NOTE: MIMO Antennas can be co-located, or widely distributed MIMO Radar with Colocated Antennas, Jian Li and Petre Stoica, IEEE SIGNAL PROCESSING MAGAZINE SEPTEMBER

33 MIMO: Radar vs. Comm s Co-located Antennas Widely Spaced Reflectors TX TX RX RX 33

34 MIMO: Radar vs. Comm s Widely Spaced Antennas Co-Located Reflectors 34

35 MIMO: Co-located Antennas Co-located Antennas Multiple Reflectors Under certain MIMO conditions, the Rank of the channel matrix indicates the number of targets in a range cell RX Range Resolution Targets are Point Reflectors Co-Located Antennas Allow Direction Finding (Reflectors of interest are within the beam pattern) RX TX TX 35

36 MIMO: Radar vs. Comm s signals COMM s Data rate greater than supported by the modulation (bandwidth) Antennas Generally Co-located at TX and at RX (except wide-spaced multi-bts) Takes advantage of multi-path in the environment Adaptive TX to optimize data transfer Signals Convey information after channel response is known. Preamble used for measuring the channel response. RADAR Range, Angle, Doppler resolution greater than supported by waveform or array size Antennas my be: Co-Located Spaced at TX Wide-Spaced at RX Takes advantage of multi-path in the target, targets (multiple per range cell, or clutter) Adaptive TX to optimize resolution, put more energy on target, minimize interference (target in another range cell, clutter) Signals don t convey information, the channel is the thing. Signals designed to simplify receiver processing orthogonal codes or frequencies 36

37 Test Implications for MIMO Loss of capacity in comm s Loss of resolution in radar Separating Hype from Reality Did I achieve the advertised improvement in performance? If not, why not? Models and Simulation Need better target, propagation and clutter models based on MIMO implementation Antenna models (antenna correlation good for phased arrays, bad for MIMO) Simulating waveform correlation under various conditions (e.g. Doppler) Performance is now a function of N different signals M different receivers Are the signals accurate (high fidelity) Are they coupled (e.g. through a common power supply) If the signals are orthogonal by design, has the orthogonality property been compromised (time/frequency misalignment, phase noise, distortion) Simulating radar return Each receiver needs a different signal 37

38 The Need for More Channels Radars have more transmit and receive channels Multiplexing instruments with fewer channels may not be optimal, or may not work, depending on the application High parallel channel counts can lead to faster test times Channels may need to be synchronous and phase coherent (e.g. for beam forming or MIMO) M8190A M9703A AXIe PXI and AXIe modular systems may offer benefits as they are easier to expand. The system shown above contains 2 12GS/s waveform generators (AWGs) and GHz IF/Baseband digitizers May need scalable solutions for production flexibility Size and space constraints PXI 38

39 Summary Frequencies and Bandwidths are going up Number of array elements are increasing Digital is moving closer to the antenna Signal complexity is increasing and becoming more adaptive Processing algorithms are growing very sophisticated. Need to verify during development, may not need to test during production Size is going down (and up!) Multifunction radars have more complicated test requirements Test involve generating and analyzing full bandwidth, multichannel signals with complex and dynamic modulation. 39

40 Questions 40 Confidentiality Label