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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

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

ICASSP Tutorial


Heterogeneous network of acoustic sensors

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


Acoustic source propagation

DSP

Acoustic Source & Propagation

Signalat sensor

(mic.)

Source

Turbulence scatters wavefronts

  • Source:

  • Loud

  • Spectral lines

  • One sensor:

  • Detection

  • Doppler estimation

  • Harmonic signature

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More sophisticated node sensor array

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

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Why acoustics

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

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Brief history of acoustics in military namorato2000

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

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Remainder of tutorial

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

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Source characteristics

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

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Heterogeneous network of acoustic sensors

High

SNR

TIME (sec)

+/- 350 m from Closest Point of Approach (CPA)

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Heterogeneous network of acoustic sensors

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


Overview of propagation issues

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


Transmission loss

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

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Measured aeroacoustic data

Measured Aeroacoustic Data

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Measured snr vs range frequency

Measured SNR vs. Range & Frequency

dB

Time

Aspect dependence

SNR ~ 50 dB

at CPA

Fluctuations due to

turbulence

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Outline

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:

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Sensor signal no scattering

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

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Sensor signal with scattering

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]

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Heterogeneous network of acoustic sensors

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

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Probability distributions

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])

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Saturation vs frequency range

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

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Heterogeneous network of acoustic sensors

Turbulence effects are small only for very short range and low frequency

Saturation variesover entire range[0, 1] for typicalrange & freq.values

Fully scattered

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Detection performance

Detection Performance

PD = probability of detectionPFA = probability of false alarm

Fullscattering

Noscattering

Scattering beginsto limit performance

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Detection performance with range

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

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Detection extensions

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]

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Outline1

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


Signal model for two sensors

q = AOA

  • = sensor spacing < l/2

Signal Model forTwo Sensors

Turbulence effects

Perfect plane wave:W = 0 or 1g = 1

ICASSP Tutorial


Model for coherence g

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)

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Heterogeneous network of acoustic sensors

Velocityfluctuations (wind)

Temperaturefluctuations

Depends on wind leveland sunny/cloudy

(From [Kozick2004a], based on[Ostashev2000, Wilson2000])

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Assumptions for model validity kozick2004a

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

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Heterogeneous network of acoustic sensors

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

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Outline2

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


Impact on aoa estimation

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!

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Special cases of crb

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!

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Phase crb with scattering w g

Phase CRB with Scattering ( W, g)

Coherence loss

g < 1 is significantwhen saturationW > 0.1

Idealplanewave

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Crb on aoa estimation

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

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Cloudy and less wind

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


Coherence vs sensor spacing

Coherence vs. Sensor Spacing

Lightwind

Strongwind

g = 0 for r ~ 1,000 in = 25 m

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Crb on aoa vs sensor spacing

CRB on AOA vs. Sensor Spacing

r = 5 inches

ICASSP Tutorial


Coherence vs sensor spacing1

Coherence vs. Sensor Spacing

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Crb on aoa vs sensor spacing1

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

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Crb achievability

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

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Aoa estimation for harmonic source

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

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Turbulence conditions for three harmonic example

Turbulence Conditions for Three-Harmonic Example

Strongscattering

Coherence is ~ 1,but still limits performance.

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Summary of aoa estimation

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]

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Outline3

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


Three localization schemes

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.

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Heterogeneous network of acoustic sensors

Localization Cramer-Rao Bounds [Kozick2004b]

  • Triangulation of AOAsMinimal comms.

  • Fully centralizedMaximum 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


Heterogeneous network of acoustic sensors

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

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Heterogeneous network of acoustic sensors

Threshold Coherence Simulation

Breakaway from CRB is accurately predictedby threshold coherence value

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Heterogeneous network of acoustic sensors

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

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Heterogeneous network of acoustic sensors

TDE Experiment – Harmonic Source

Moderate coherence,small bandwidth

TDE works within an array(sensor spacing < 8 feet)

TDE is not possible between arrays

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Heterogeneous network of acoustic sensors

TDE Experiment – Wideband Source

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Heterogeneous network of acoustic sensors

TDE Experiment – Wideband Source

Measured coherenceexceeds thresholdcoherence ~ 0.8 for this case Accurate localization from TDEs

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Summary of array of arrays

Summary of Array of Arrays

  • Threshold coherence analysis tells conditions when joint, coherent processing improves localization accuracy

  • Pairwise TDE between arrays captures localization information reduced comms.

  • Incoherent triangulation of AOAs is optimum for narrowband, harmonic sources

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Odds ends other sensing approaches

Odds & EndsOther Sensing Approaches

  • Infrasonics: f < 30 Hz, λ > 11 m [Bedard2000]

    • Over the horizon propagation via ducting from temperature gradients

    • Wind noise is very high

    • Propagation models may be lacking

  • Fuse acoustics with other low-BW sensor modalities

    • Seismic has proven useful for heavy, loud vehicles and aircraft (also footsteps)

    • Vector magnetic sensors are emerging

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Odds ends other processing

Odds & EndsOther Processing

  • Doppler estimation: [Kozick2004c]

    • Localize based on differential Doppler from multiple sensors (combine with AOAs?)

    • Not sensitive to coherence

    • Simple, and exploits spectral lines in source

    • We have analyzed CRBs and algorithms

  • Tracking of moving sources [see Biblio.]

  • Classification based on harmonic ampls. [see Biblio.]

    • Harmonic amplitudes fluctuate

    • Exploit aspect angle differences of sources?

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Summary

Source

Central

Node?

Summary

  • Source characteristics:Spectral lines, ultra-wideband, high SNR

  • Propagation: dominated by turbulent scattering

    • Amplitude & phase fluctuations (saturation, W)

    • Spatial coherence loss (coherence, g)

    • Depends on freq., range, weather, sensor spacing

  • Signal processing:

    • Coherence losses limit AOA and TDE performance Ideal plane wave is overly optimistic

    • Implications for detection and array aperture size

    • Sensor network: comms. & distributed processing

Provided a detailed case-study ofa particular sensor network.

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Heterogeneous network of acoustic sensors

Questions?Thanks for attending!

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