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Heterogeneous Network of Acoustic Sensors

Sensor Networks, Aeroacoustics, and Signal Processing Part II: Aeroacoustic Sensor Networks Brian M. Sadler Richard J. Kozick 17 May 2004. Central Node?. Heterogeneous Network of Acoustic Sensors. One mic. Sensor array. Source. 10’s to 100’s of m range. Issues:

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Heterogeneous Network of Acoustic Sensors

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  1. Sensor Networks, Aeroacoustics,and Signal ProcessingPart II: Aeroacoustic Sensor NetworksBrian M. SadlerRichard J. Kozick17 May 2004 ICASSP Tutorial

  2. Central Node? Heterogeneous Network of Acoustic Sensors One mic. Sensorarray Source 10’s to 100’s of mrange • Issues: • Centralized versus distributed processing • Minimize comms., energy • Triangulation, TDOA? • Effects of acoustic prop. • (Node localization, sensor layout & density, Classification, tracking) ICASSP Tutorial

  3. DSP Acoustic Source & Propagation Signalat sensor (mic.) Source Turbulence scatters wavefronts • Source: • Loud • Spectral lines • One sensor: • Detection • Doppler estimation • Harmonic signature ICASSP Tutorial

  4. DSP More Sophisticated Node:Sensor Array 1 m apertureor (much) less • Issues: • Array size/baseline • Coherence decreases with larger spacing • AOA estimation accuracy with turbulent scattering • What can we do with a hockey-puck sensor? • Small, covert, easily deployable • Performance? Wavelength ~ 1 to 10 m ICASSP Tutorial

  5. Why Acoustics? • Advantages: • Mature sensor technology (microphones) • Low data bandwidth: ~[30, 250] Hz Sophisticated, real-time signal proc. • Loud sources, difficult to hide: vehicles, aircraft, ballistics • Challenges: • Ultra-wideband regime: 157% fractional BW • λ in [1.3, 11] m  Small array baselines • Propagation: turbulence, weather, (multipath) • Minimize network communications • We bound the space of useful system design and performance [Kozick2004a] • with respect to SNR, range, frequency, bandwidth, observation time, propagation conditions (weather), sensor layout, source motion/track • evaluate performance of algorithms (simulated & measured data) Distinct fromRF arrays ICASSP Tutorial

  6. Brief History of Acoustics in Military [Namorato2000] • Trumpeting down walls of Jericho • Chinese, Roman (B.C.) • Civil War: Generals used guns to signal attacks • World War 1: • Science of acoustics developed • Air & underwater acoustic systems • World War 2: Mine actuating, submarine detection • Many developments … • Army Research Laboratory, 1991-present [Srour1995] • Hardware and software for detection, localization, tracking, and classification • Extensive field testing in various environments with many vehicle types ICASSP Tutorial

  7. Remainder of Tutorial • A little more background: • Source characteristics • Sound propagation through turbulent atmosphere • Detection of sources (Saturation, W) • Array processing • Coherence loss from turbulence (Coherence, g) • Array size: 2 sensors  source AOA • Array of arrays  source localizationDistributed processing? Data to transmit?Triangulation/TDOA Minimize comms. Experimentallyvalidatedmodels ICASSP Tutorial

  8. Source Characteristics • Ground vehicles (tanks, trucks), aircraft (rotary, jet), commercial vehicles, elephant herds LOUD • Main contributors to source sound: • Rotating machinery: Engines, aircraft blades • Tires and “tread slap” (spectral lines) • Vibrating surfaces • Internal combustion engines: Sum-of-harmonics due to cylinder firing • Turbine engines: Broadband “whine” • Key features: Spectral lines and high SNR Distinct fromunderwater ICASSP Tutorial

  9. High SNR TIME (sec) +/- 350 m from Closest Point of Approach (CPA) ICASSP Tutorial

  10. Frequency Harmonic lines Doppler shift: 0.5 Hz @ 14.5 Hz  3 Hz @ 87 Hz Time +/- 85 m from CPA CPA = 5 m 15 km/hr ICASSP Tutorial

  11. Overview of Propagation Issues • “Long” propagation time: 1 s per 330 m • Additive noise: Thermal, Gaussian(also wind and directional interference) • Scattering from random inhomogeneities (turbulence)  temporal & spatial correl. • Random fluctuations in amplitude: W • Spatial coherence loss (>1 sensor): g • Transmission loss: Attenuation by spherical spreading and other factors ICASSP Tutorial

  12. Numerical Solution[Wilson2002] Transmission Loss • Energy is diminished from Sref (at 1 m from source) to S at sensor: • Spherical spreading • Refraction (wind, temp. gradients) • Ground interactions • Molecular absorption • We model S as a deterministic parameter:  Average signal energy Low Pass Filter [Embleton1996] +/- 125 m from CPA ICASSP Tutorial

  13. Measured Aeroacoustic Data ICASSP Tutorial

  14. Measured SNR vs. Range & Frequency dB Time Aspect dependence SNR ~ 50 dB at CPA Fluctuations due to turbulence ICASSP Tutorial

  15. Outline Sinusoidal signal emitted by moving source: • Detection of sources • Saturation, W • Detection performance • Array processing • Coherence loss from turbulence, g • Array size: 2 sensors  Source AOA • Array of arrays: Source localizationTriangulation/TDOA, minimize comms. • Propagation delay, t • Additive noise • Transmission loss • Turbulent scattering Signal at the sensor: ICASSP Tutorial

  16. Sensor Signal: No Scattering • Sensor signal with transmission loss,propagation delay, and AWG noise: • Complex envelope at frequency fo • Spectrum at fo shifted to 0 Hz • FFT amplitude at fo ICASSP Tutorial

  17. Sensor Signal: With Scattering • A fraction, W, of the signal energy is scattered from a pure sinusoid into a zero-mean, narrowband, Gaussian random process, : • Saturation parameter, W in [0, 1] • Varies w/ source range, frequency, and meteorological conditions (sunny, windy) • Based on physical modeling of sound propagation through random, inhomogeneous medium • Easier to see scattering effect with a picture: [Norris2001, Wilson2002a] ICASSP Tutorial

  18. Weak Scattering: W ~ 0 Strong Scattering: W ~ 1 (1- W)S Power Spectral Density (PSD) WS Area= WS AWGN, 2No (1- W)S 0 0 Freq. -B/2 B/2 -B/2 B/2 Bv Bv • Study detection of source, w/ respect to • Saturation, W (analogous to Rayleigh/Rician fading in comms.) • Processing bandwidth, B, and observation time, T • SNR = S / (2 No B) • Scattering bandwidth, Bv < 1 Hz (correlation time ~ 1/Bv > 1 sec) • Number of independent samples ~ (T Bv)  often small • Scattering (W > 0) causes signal energy fluctuations ICASSP Tutorial

  19. Probability Distributions • Complex amplitude has complex Gaussian PDF with non-zero mean: • Energy has non-central c-squared PDF with 2 d.o.f. • has Rice PDF (Experimental validation in [Daigle1983, Bass1991, Norris2001]) ICASSP Tutorial

  20. Saturation vs. Frequency & Range • Saturation depends on [Ostashev2000]: • Weather conditions (sunny/cloudy), but varies little with wind speed • Source frequency w and range do Theoretical forms Constants from numerical evaluation of particular conditions ICASSP Tutorial

  21. Turbulence effects are small only for very short range and low frequency Saturation variesover entire range[0, 1] for typicalrange & freq.values Fully scattered ICASSP Tutorial

  22. Detection Performance PD = probability of detectionPFA = probability of false alarm Fullscattering Noscattering Scattering beginsto limit performance ICASSP Tutorial

  23. Detection Performance with Range Lower frequenciesdetected at larger ranges SNR = 50 dB at 10 m rangeSNR ~ 1/(range)2 1 km 200 Hz • Saturation increases with • Range • Frequency • Temperature (sunny) 40 Hz ICASSP Tutorial

  24. Detection Extensions • Sensor networks: Detection  queuing (wake-up) of more sophisticated sensors • Multiple snapshots & frequencies • Source motion & nonstationarities • Coherence time of scattering • Multiple sensors with different SNR and W • Distributed detection, what to communicate? • Required sensor density for reliable detection • Source localization based on energy level at the sensors [Pham2003] Physical models for cross-freq coherenceand coherence timeare in preliminary stage[Norris2001, Havelock1998] ICASSP Tutorial

  25. Outline • Detection of sources • Saturation, W • Detection performance • Array processing • Coherence loss from turbulence, g • Array size: 2 sensors  Source AOA • Array of arrays: Source localizationTriangulation/TDOA, minimize comms. • Coherence, g, depends on • Sensor separation, r • Source frequency, w • Source range, do • Weather conditions: sunny/cloudy, wind speed ICASSP Tutorial

  26. q = AOA • = sensor spacing < l/2 Signal Model forTwo Sensors Turbulence effects Perfect plane wave:W = 0 or 1g = 1 ICASSP Tutorial

  27. q do = range • = sensor spacing Model for Coherence, g • Assume AOA q = 0, freq. in [30, 500] Hz • Recall saturation model: • Coherence model [Ostashev2000]: g 0 with freq., sensorspacing, and range Temperaturefluctuations Velocityfluctuations (wind) ICASSP Tutorial

  28. Velocityfluctuations (wind) Temperaturefluctuations Depends on wind leveland sunny/cloudy (From [Kozick2004a], based on[Ostashev2000, Wilson2000]) ICASSP Tutorial

  29. Assumptions for Model Validity [Kozick2004a] • Line of sight propagation (no multipath) appropriate for flat, open terrain • AWGN is independent from sensor to sensor  ignores wind noise, directional interference • Scattered process is complex, circular, Gaussian [Daigle1983, Bass1991, Norris2001] • Wavefronts arrive at array aperture with near-normal incidence • Sensor spacing, r, resides in the inertial subrange of the turbulence: smallest turbulent eddies << r << largest turbulent eddies = Leff ICASSP Tutorial

  30. Coherence, g, versus frequencyand range forsensor spacingr = 12 inches • > 0.99 for range < 100 m.Is this “good”? Curves shiftup w/ less wind,down w/ more wind ICASSP Tutorial

  31. SenTech HE01 acoustic sensor [Prado2002] Outline • Freq. in [30, 250] Hz  l in [1.3, 11] m • Angle of arrival (AOA) accuracy w.r.t. • Array aperture size • Turbulence (W, g) • Small aperture: • Easier to deploy • More covert • Better coherence • How small can we go? • Detection of sources • Saturation, W • Detection performance • Array processing • Coherence loss from turbulence, g • Array size: 2 sensors  Source AOA • Array of arrays: Source localizationTriangulation/TDOA, minimize comms. ICASSP Tutorial

  32. Impact on AOA Estimation • How does the turbulence (W, g) affect AOA estimation accuracy? [Sadler2004] • Cramer-Rao lower bound (CRB), simulated RMSE • Achievable accuracy with small arrays? Larger sensorspacing, r: DESIRABLE BAD! ICASSP Tutorial

  33. Special Cases of CRB • No scattering (ideal plane wave model): • High SNR, with scattering: SNR-limitedperformance Coherence-limitedperformance If SNR = 30 dB, then g < 0.9989995 limits performance! ICASSP Tutorial

  34. Phase CRB with Scattering ( W, g) Coherence loss g < 1 is significantwhen saturationW > 0.1 Idealplanewave ICASSP Tutorial

  35. CRB on AOA Estimation SNR = 30 dB for all ranges Sensor spacing r = 12 in. Coherence-limited at larger ranges Increasingrange (fixed SNR) Aperture-limitedat low frequency Ideal plane wave modelis accurate for very shortranges ~ 10 m ICASSP Tutorial

  36. Cloudy and Less Wind SNR = 30 dB for all ranges Sensor spacing r = 12 in. Atmospheric conditionshave a large impact onAOA CRBs Aperture-limitedat low frequency Plane wave model isaccurate to 100 m range ICASSP Tutorial

  37. Coherence vs. Sensor Spacing Lightwind Strongwind g = 0 for r ~ 1,000 in = 25 m ICASSP Tutorial

  38. CRB on AOA vs. Sensor Spacing r = 5 inches ICASSP Tutorial

  39. Coherence vs. Sensor Spacing ICASSP Tutorial

  40. CRB on AOA vs. Sensor Spacing Ideal planewave modelis optimistic (poor weather) Coherence lossesdegrade AOA perf.for r > 8 feet (First noted in[Wilson1998], [Wilson1999]) l/2 Plane waveis OK for good weather ICASSP Tutorial

  41. CRB Achievability Phase difference estimator: Scenario:Small Sensor Spacing: r = 3 in., SNR = 40 dB, Range = 50 m AOA estimators break away from CRB approx.when W > 0.1 Saturation W issignificant for most offrequency range Turbulence prevents performance gain from larger aperture Coherence is high: g > 0.999 Aperture-limited ICASSP Tutorial

  42. AOA Estimation for Harmonic Source Equal-strength harmonicsat 50, 100, 150 Hz SNR = 40 dB at 20 mrange, SNR ~ 1/(range)2 (simple TL) Sensor spacingr = 3 in. and 6 in. Mostly sunny,moderate wind One snapshot r = 3 in. RMSE r = 6 in. CRB Achievable AOA accuracy ~ 10’s of degrees for this case ICASSP Tutorial

  43. Turbulence Conditions for Three-Harmonic Example Strongscattering Coherence is ~ 1,but still limits performance. ICASSP Tutorial

  44. Summary of AOA Estimation • CRB analysis of AOA estimation • Tradeoff: larger aperture vs. coherence loss • Ideal plane wave model is overly optimistic for longer source ranges • Performance varies significantly with weather cond. • Important to consider turbulence effects • AOA algorithms do not achieve the CRB in turbulence (W > 0.1) with one snapshot • Similar results obtained for circular arrays with > 2 sensors [Sadler2004] ICASSP Tutorial

  45. Outline • Issues: • Comm. bandwidth • Distributed processing • Exploit long baselines? • Time sync. among arrays • Model assumptions: • Individual arrays: • Perfect coherence • Far-field • Between arrays: • Partial coherence • Different power spectra • Near-field • Detection of sources • Saturation, W • Detection performance • Array processing • Coherence loss from turbulence, g • Array size: 2 sensors  Source AOA • Array of arrays: Source localizationTriangulation/TDOA, minimize comms. ICASSP Tutorial

  46. Source Source Fusion Node Fusion Node Fusion Node Three Localization Schemes AOA &TDOA AOA AOA &TDOA AOA Transmit raw data from one sensorto other nodes Source AOA AOA TransmitAOAs Transmitraw data from all sensors TransmitAOAs & TDOAs Noncoherenttriangulationof AOAs Optimum,coherentprocessing Triangulationof AOAs andTDOAs • 2) Fully centralized • Comms.: High • Centralized processing • Fine time sync. req’d • Near-field w.r.t. arrays • 3) AOAs & TDOAs: • Comms.: Medium • Distributed processing • Fine time sync. req’d • Alt.: Each array xmits raw data from 1 sensor • 1) Triangulate AOAs: • Comms.: Low • Distributed processing • Coarse time sync. ICASSP Tutorial

  47. Localization Cramer-Rao Bounds [Kozick2004b] • Triangulation of AOAs Minimal comms. • Fully centralized Maximum comms. • #1 + time delay estimation (TDE) Raw data from 1 sensor • Schemes 2 & 3 have same CRB! • Results are coherence sensitive: coherence improves CRB over AOA triangulation • Are the CRBs achievable? AOAtriangulation Parameters: 3 arrays, 7 elements, 8 ft. diam.Narrowband (49.5 to 50.5 Hz)SNR = 16 dB at each sensor0.5 sec. observation time ICASSP Tutorial

  48. TB product Fract BW SNR Ziv-Zakai Bound on TDE • Threshold coherence to attain CRB: • Function(SNR, % BW, TB product, coherence) • Extends [WeissWeinstein83] to TDE with partially-coherent signals ICASSP Tutorial

  49. Threshold Coherence Simulation Breakaway from CRB is accurately predictedby threshold coherence value ICASSP Tutorial

  50. Threshold Coherence Doubling fractional BW time-BW product reduced by factor of ~ 10 f0 = 50 Hz, Df = 5 Hz  T>100 s Narrowband sourcerequires perfect coherence ICASSP Tutorial

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