15.7 ELECTRONICS APPENDIX E 15.7.1 APPENDIX E1: ORIGINAL ...
An Acoustic / Radar System for Automated Detection, …€¦ · Task 14.6 Task 14.7 External Data...
Transcript of An Acoustic / Radar System for Automated Detection, …€¦ · Task 14.6 Task 14.7 External Data...
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An Acoustic / Radar System for AutomatedDetection, Localization, and Classification of
Birds in the Vicinity of Airfields
Dr. Sebastian M. Pascarelle & Dr. BruceStewart (AAC)
T. Adam Kelly & Andreas Smith (DeTect)Dr. Robert Maher (MSU)
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Outline
• Introduction
• Acoustic Sensor
• Acoustic Field Test Results
• Parabolic Dish Microphone Results
• Acoustic Classification Techniques
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Introduction
• Hybrid Birdstrike Monitoring System:– Acoustic array
– Radar
– Parabolic dish microphone
• Data fusion
• Acoustic classification
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Introduction
• Phase 2 STTR– Sponsor: Air Force Office of Scientific Research
Dr. Willard Larkin
– Team:AAC –Project management, system integration, acoustic array,
acoustic signal processing and classification
DeTect, Inc. –Bird Detection Radar, signal processing, radar dataanalysis, PCBCIA field test, bird strike experts
MSU –University partner, parabolic dish microphone, acousticclassification, atmospheric compensation model
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Acoustic Sensor
Sparsely Populated Volumetric Array (SPVA)
• 18 hydrophones embedded in polyurethane• Provides 12.5 dB gain• Covers very large frequency range• 4π steradian coverage• Real-time angle of arrival without
beamforming• Fractional degree angle accuracy• Fiber optic telemetry
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SPVA
• Proven sensor and signal processing technology currentlydeployed in the Navy fleet
• Outperforms legacy systems• Multiple sensors can give target range
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Air Acoustic Array
• 18 microphonesmounted on rods
• Covers 0.2-20 kHzfrequency range
• Sound absorber tomitigate reflections
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Complete Acoustic Sensor System
Digital recorder for offline signalprocessing (production system will do
real-time signal processing)
Air SPVA sensorand pre-amplifiers
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SPVA Real-Time Displays
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Data Fusion
DFECData
FusionExternal
Communication
DFFSData
Structures
SPVAD/C/L
CRHD/C/L
RadarD/C/L
ESMD/C/L
IRD/C/L
Shipboard Organic Sensors
UAV Sensors
VideoD/C/L
IRD/C/L
DFNCData Fusion
Control
DFENData Fusion
Engine
DFDPData Fusion
Display
DFDBData Fusion
Database
DFRGData FusionRegistration
DataFusion
IntelligentController
Task 1.1
Task 1.3
Task 1.4 Task 1.5
Task 1.6
Task 1.2
Task 1.7
Data Fusion Function
SystemNetwork
DFECData
FusionExternal
Communication
DFFSData
Structures
SPVAD/C/L
CRHD/C/L
RadarD/C/L
ESMD/C/L
IRD/C/L
Shipboard Organic Sensors
UAV Sensors
VideoD/C/L
IRD/C/L
DFNCData Fusion
Control
DFENData Fusion
Engine
DFDPData Fusion
Display
DFDBData Fusion
Database
DFRGData FusionRegistration
DataFusion
IntelligentController
Task 1.1
Task 1.3
Task 1.4 Task 1.5
Task 1.6
Task 1.2
Task 1.7
Data Fusion Function
SystemNetwork
Task 14.1
Task 14.2
Task 14.3
Task 14.4 Task 14.5
Task 14.6
Task 14.7
DFECData
FusionExternal
Communication
DFFSData
Structures
SPVAD/C/L
CRHD/C/L
RadarD/C/L
ESMD/C/L
IRD/C/L
Shipboard Organic Sensors
UAV Sensors
VideoD/C/L
IRD/C/L
DFNCData Fusion
Control
DFENData Fusion
Engine
DFDPData Fusion
Display
DFDBData Fusion
Database
DFRGData FusionRegistration
DataFusion
IntelligentController
Task 1.1
Task 1.3
Task 1.4 Task 1.5
Task 1.6
Task 1.2
Task 1.7
Data Fusion Function
SystemNetwork
DFECData
FusionExternal
Communication
DFFSData
Structures
SPVAD/C/L
CRHD/C/L
RadarD/C/L
ESMD/C/L
IRD/C/L
UAV Sensors
VideoD/C/L
IRD/C/L
DFNCData Fusion
Control
DFENData Fusion
Engine
DFDPData Fusion
Display
DFDBData Fusion
Database
DFRGData FusionRegistration
DataFusion
IntelligentController
Task 1.3
Task 1.4 Task 1.5
Task 1.6
Task 1.2
Task 1.7
Data Fusion Function
SystemNetwork
Task 15.2
Task 15.3
Task 15.4 Task 15.5
Task 15.6
Task 15.7
Task 15.1
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Atmospheric Compensation
• Wind speed• Temperature• Humidity
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Field Test: PCBCIA
• Located in proximity to airport runway,trees, and water
• Test at beginning of Nov. to catch Fallmigration
Test Location
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Field Test: PCBCIA
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Aircraft Detection & Tracking
• Track of small aircraft passing nearly directlyoverhead proves system capability
• Angle accuracy is < 10 deg
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Aircraft Detection & Tracking:Radar Confirmation
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Ø In a typical 30 minute time interval, at least30 episodes of flight calls were detected.
Ø Calls from a single bird were frequent,every 1 to 3 seconds.
Ø Each episode consisted of many calls,typically 4 to 30.
Ø Presumed local, not migratory flight
Ø Calls were mostly at higher frequencies,indicating small, low-threat birds.
Morning Flight Calls
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Morning Flight Calls
Human interpreters will easily recognize tracksdue to frequent and numerous calls.
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Acoustic detection,localization, and tracking
Tracking software maintains integrity of overlapping tracks
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• Test setup has a singleSPVA, so range is notknown.
• (Relative) ranges areestimated fromamplitudes of calls.
• There is one freeparameter, to be fixedfrom radar data.
Morning Flight Calls
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Morning Flight Calls:Radar Confirmation
• Radar confirmationshows acousticdetections rangedup to 600 ft
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Evening Flight Calls
Ø During a typical 6-minute interval, 5episodes of flight calls were detected.
Ø Interval between calls in one episode istypically 5 to 10 seconds.
Ø Each episode has very few calls, typically1 to 4.
Ø Presumed migratory flight
Ø Calls were mostly at higher frequencies,indicating small, low-threat birds.
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Evening Flight Calls
Acoustic detections ranged up to 1500 feet
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Multi-Source DetectionSystem can detect and track multiple targets simultaneously
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Bat Detection
• Results of a bat detection event consisting of 8 calls• Calls are at the upper end of the detection band
-1 -0.5 0 0.5 1
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12.5 sec
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Azimuth bearings with detection times,indicating an erratic flight path
conventionalspectrogram
complexreassigned
spectrogram
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Parabolic Dish Microphone
• Classification requires high S/N data• Lots of competing background noise at airfield• Need directional, high-gain microphone• Large electronically steered arrays expensive• Solution:
– Commercially available dish microphone– Mounted on two-axis servo– Mechanically steered by:
• radar track data• acoustic bearing data
– Provides signal isolation and gain
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Parabolic Dish Performance
• Plot shows parabolic dish performanceimprovement over simple microphones
• As much as 25 dB gain in laboratory tests
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0 2000 4000 6000 8000 10000
Frequency (Hz)
Ampl
itude
(dB
)
DPAPanasonicDish
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Parabolic Dish Performance
Two sparrow calls: the first is heard faintlyby the air array, strongly with the dish.
air arrayelement 9
parabolicdish
microphone
10 kHz
8 kHz
6 kHz
4 kHz
10 kHz
8 kHz
6 kHz
4 kHz
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Parabolic Dish Performance
Parabolic dish provides 19 dB gain for bird calls
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Parabolic Dish Steering
Microcontroller circuit directs the dish tobearings from air array signal processing
Microcontroller
Dual Full-Bridge Driver
24V DC
Dual Full-Bridge Driver
RS-232Serial Cable
ElevationStepper
AzimuthStepper
Microcontroller(eval board)
StepperControlBoards
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Classification Software
• Frequency Track Analysis– AAC
– MSU
• Cortical Processing Theory– AAC & UMD
• Composite Classifier– MSU
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Frequency Track Analysis
Blue Jay Call
Spectrogram Frequency Tracks
•Compute spectrogram and smooth
•Find local maxima at each time and connect (peak tracks)
•Remove short and weak tracks
•Compute features (min, max, and mean frequency, length, slope,… )
•Compare features statistically with training set
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Frequency track classifier results
• MSU: 12 species and 16 synthesized sounds,99% success with 12 db SNR added noise
• AAC: 4 species trained, 10 species tested with0 false positives (except blue jay)
Blue jay matching tracks Herring gull syllable recognition
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Center for AuditoryandAcoustic Research
Response field in threedimensions (rate, scale, time)visualized using isosurfaces
4.5 5 5.5 6 6.5 7-6
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6Principal Components of Rate-Scale Matrices
prin
cipa
l com
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nt 2
house wrenchipping sparrow
In-flight chip calls from house wren (top) and sparrow (bottom) are clearlydistinguished and classified using rate-scale representation.
freq
uenc
yfr
eque
ncy
time
time
scal
esc
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rate
rate
wren
sparrow
Prof. S. Shamma
Cortical Processing Theory
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Bird call recognition from rate-scale matrix
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MSU Compound Classifier
• Auditory cortex ripple-based• Frequency track analysis• Other candidate methods• Weighted comparison of all will give optimal
result
Classifier N
Classifier 2
Classifier 1
• Environmental status• High-level analysis• a priori inputs
Signal PropertiesDetermination
Input Signal
Classification
Reliability RatingEstimate
WeightedComparison
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Conclusions
• AAC’s highly successful underwater acoustic array sensorhas been transitioned to an air sensor
• Field testing has proven its capability to detect a variety ofacoustic sources at significant distances
• Testing alongside radar has shown that the two systemsare highly complementary
• Parabolic dish provides significant gain over array• Combined system of array, radar, and dish is a robust
solution to monitoring bird activity at airfields• System can be used to detect, track, and classify other
activity as well:– vehicles, watercraft, aircraft, people, bats– Potential Homeland Security applications –perimeter security,
border security