An Acoustic / Radar System For Automated Detection,

An Acoustic / Radar System for AutomatedDetection, Localization, and Classification ofBirds in the Vicinity of AirfieldsDr. Sebastian M. Pascarelle & Dr. BruceStewart (AAC)T. Adam Kelly & Andreas Smith (DeTect)Dr. Robert Maher (MSU)1

Outline Introduction Acoustic Sensor Acoustic Field Test Results Parabolic Dish Microphone Results Acoustic Classification Techniques2

Introduction Hybrid Birdstrike Monitoring System:– Acoustic array– Radar– Parabolic dish microphone Data fusion Acoustic classification3

Introduction Phase 2 STTR– Sponsor: Air Force Office of Scientific ResearchDr. Willard Larkin– Team:AAC –Project management, system integration, acoustic array,acoustic signal processing and classificationDeTect, Inc. –Bird Detection Radar, signal processing, radar dataanalysis, PCBCIA field test, bird strike expertsMSU –University partner, parabolic dish microphone, acousticclassification, atmospheric compensation model4

Acoustic SensorSparsely 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 withoutbeamforming Fractional degree angle accuracy Fiber optic telemetry5

SPVA Proven sensor and signal processing technology currentlydeployed in the Navy fleet Outperforms legacy systems Multiple sensors can give target range6

Air Acoustic Array 18 microphonesmounted on rods Covers 0.2-20 kHzfrequency range Sound absorber tomitigate reflections7

Complete Acoustic Sensor SystemAir SPVA sensorand pre-amplifiersDigital recorder for offline signalprocessing (production system will doreal-time signal processing)8

SPVA Real-Time Displays9

Data FusionShipboard Organic arD/C/LESMD/C/LIRD/C/LUAV SensorsVideoD/C/LIRD/C/LTask 15.214.2Task1.2DataFusionDFNCData 14.5DFENDFDBData FusionEngineData FusionDatabaseTask 15.31.314.3Task14.6Task15.61.6DFRGDFDPData FusionRegistrationData FusionDisplayData Fusion tController10

Atmospheric Compensation Wind speed Temperature Humidity11

Field Test: PCBCIATest Location Located in proximity to airport runway,trees, and water Test at beginning of Nov. to catch Fallmigration12

Field Test: PCBCIA13

Aircraft Detection & Tracking Track of small aircraft passing nearly directlyoverhead proves system capability Angle accuracy is 10 deg14

Aircraft Detection & Tracking:Radar Confirmation15

Morning Flight CallsØIn 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 flightØ Calls were mostly at higher frequencies,indicating small, low-threat birds.16

Morning Flight CallsHuman interpreters will easily recognize tracksdue to frequent and numerous calls.17

Acoustic detection,localization, and trackingTracking software maintains integrity of overlapping tracks18

Morning Flight Calls Test setup has a singleSPVA, so range is notknown. (Relative) ranges areestimated fromamplitudes of calls. There is one freeparameter, to be fixedfrom radar data.19

Morning Flight Calls:Radar Confirmation Radar confirmationshows acousticdetections rangedup to 600 ft20

Evening Flight CallsØ 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 flightØ Calls were mostly at higher frequencies,indicating small, low-threat birds.21

Evening Flight CallsAcoustic detections ranged up to 1500 feet22

System can detect Multi-Sourceand track multipletargets simultaneouslyDetection23

Bat DetectionAzimuth bearings with detection times,indicating an erratic flight 2.5 xreassignedspectrogram1 Results of a bat detection event consisting of 8 calls Calls are at the upper end of the detection band24

Parabolic Dish Microphone Classification requires high S/N dataLots of competing background noise at airfieldNeed directional, high-gain microphoneLarge electronically steered arrays expensiveSolution:– Commercially available dish microphone– Mounted on two-axis servo– Mechanically steered by: radar track data acoustic bearing data– Provides signal isolation and gain25

Parabolic Dish Performance8580Amplitude 00Frequency (Hz) Plot shows parabolic dish performanceimprovement over simple microphones As much as 25 dB gain in laboratory tests26

Parabolic Dish Performance10 kHz8 kHz6 kHzparabolicdishmicrophone4 kHz10 kHz8 kHzair arrayelement 96 kHz4 kHzTwo sparrow calls: the first is heard faintlyby the air array, strongly with the dish.27

Parabolic Dish PerformanceParabolic dish provides 19 dB gain for bird calls28

Parabolic Dish Steering24V DCElevationStepperRS-232Serial CableDual FullBridge DriverMicrocontroller(eval board)MicrocontrollerAzimuthStepperDual FullBridge DriverStepperControlBoardsMicrocontroller circuit directs the dish tobearings from air array signal processing29

Classification Software Frequency Track Analysis– AAC– MSU Cortical Processing Theory– AAC & UMD Composite Classifier– MSU30

Frequency Track Analysis 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 setSpectrogramFrequency TracksBlue Jay Call31

Frequency track classifier resultsBlue jay matching tracksHerring gull syllable recognition 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)32

Cortical Processing TheoryProf. S. ShammaCenter for Auditoryand Acoustic ResearchfrequencywrenIn-flight chip calls from house wren (top) and sparrow (bottom) are clearlydistinguished and classified using rate-scale representation.Response field in threedimensions (rate, scale, time)visualized using isosurfacesPrincipal Components of Rate-Scale al component 242-2-4-64.5ratetimehouse wrenchipping sparrow055.566.5733

Bird call recognition from rate-scale matrix34

MSU Compound ClassifierWeightedComparisonClassifier 1Classifier 2Input SignalClassifier NSignal PropertiesDetermination ClassificationReliability RatingEstimate Environmental status High-level analysis a priori inputsAuditory cortex ripple-basedFrequency track analysisOther candidate methodsWeighted comparison of all will give optimalresult35

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 robustsolution to monitoring bird activity at airfields System can be used to detect, track, and classify otheractivity as well:– vehicles, watercraft, aircraft, people, bats– Potential Homeland Security applications –perimeter security,border security36

Registration Data Fusion Intelligent Controller Task 1.1 Task 1.3 Task 1.4 Task 1.5 Task 1.6 Task 1.2 Task 1.7 Data Fusion Function System Network DFRG Registration Task 14.1 Task 14.2 Task 14.3 Task 14.4 Task 14.5 Task 14.6 Task 14.7 . – vehicles, watercraft, aircraft, people, bats