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Localization and Tracking Noise Sources with Autonomous Vehicles: From Node Processing to Central Command Fusion and Tracking Dr. James Carneal, Prof. Marty Johnson, Dan Mennitt, and Philip Gillett 2006 NC-ASA Spring Meeting April 6th and 7th Ramada Blue Ridge, Raleigh, NC Motivation • The use of autonomous unmanned vehicles (AUV) to perform acoustic surveillance removes humans from dangerous environments. Microphone Array Autonomous Vehicle • Congress passed a mandate requiring 1/3 of all ground armed forces to be autonomous by 2015 . Typical Autonomous Vehicles Aircraft Land based Grand Challenge Underwater Acoustic Surveillance: Approach • • • • Small microphone arrays. Limited local processing. Broadcast over network. Tracking system to account for: – – – – – Sparse data sets Positioning of AVs Motion of AVs Number of AVs Vehicle self noise • Experimental based approach. Noise Source Tracking system System Structure Event Intelligent nodes (limited processing) Environment Environment Environment Event Detection 1 2 Angle of Arrival Coarse Classification Event Info. System Level Battlefield model Localization Storage Buffer Detailed classification Request for detailed Info 3 AV Array Node Level Processing System Level Processing Node Level Array Design • Array Design – Modular – Vehicle mounted – Redundancy and robustness – Free-field characterization GPS Compass Modular • Self noise removal – Design array mics for low noise – Use engine mounted sensors to remove noise (LMS) – “Quiet mode” of operation Battery storage and generator power Vehicle mounted Magnitude (dB) 0 500 1000 0 0.02 0 Source 500 1000 1500 2000 Frequency (Hz) Magnitude 0.02 Imp(x) = 54 0 -0.02 0 0.2 0.4 0.6 Time (seconds) 2 1.5 Ton(X) = 39 1 0.5 0.8 1 0 0 2000 4000 6000 Frequency (Hertz) 8000 0.2 2500 2500 30 0 50 20 100 10 3000 3500 0 150 200 Angle of Arrival (degrees) 3000 3500 Instantaneous Power 250 Running300 350 Average Power 8 6 0 500 1000 0.4 0.6 Time (seconds) 1500 2000 Frequency (Hz) 4 0.8 1 0 2500 3000 3500 Ton(X) = 121 2 2.5 0 1500 2000 Frequency (Hz) Instantaneous Power Running Average Power Imp(x) = 5.5 -0.01 Magnitude 0 0 3 0.04 -0.04 20 Magnitude 0.005 Instantaneous Power Running Average Power 0.04 20 Magnitude (dB) 0.01 -0.005 0.06 40 0 Magnitude – Loudness – Angle of arrival (diffraction compensated) – Impulse detection – Tonality –… Magnitude of PSF • Coarse Classification 40 Magnitude (dB) Node Level Processing 0 2000 4000 6000 Frequency (Hertz) 8000 System Level Processing Sensor Nodes Event Data used to respond to events Network communication Environment model Central System Event : : : : Event # 134 Node #3 14.34Hrs 23.2sec 37.228804 N 80.427313 W Az=1230 Impulsive Event # 135 Node #10 14.34Hrs 23.8sec 37.228790 N 80.427353 W Az=-320 Vehicle : : : : Networked Data Structure : : System Level Details • Fusion Center: – Localization: associate node information to events – Classification: merge events to objects – Tracking: merging objects to paths. Event t=4 same Event t=6 System Level: Initial Simulation • Noisy estimates with moving sensor nodes and sources Kalman Filtering Filters estimate Noisy estimate Moving sensor nodes Experimental Data Collection • Algorithms developed using real data Noise source Acquisition equipment Arrays Localization results Three arrays Camera Jamie Impacts Durham Hall Localization results Bus Three arrays Camera Durham Hall Localization results Bus Camera Durham Hall Low cost nodal processors Laptop 6-channel data acquisition GPS & Time USB Compass &Temp • Low cost: $1600/node. • Allows fixed/moving array measurements over large areas Node Level Challenges • Reduction of footprint integration. • Improved signal processing: – Characterization – Beamforming • Eventually, other sensor types. System Level Challenges Time reversal • Event confusion due to propagation delays No Directivity – Simplification using event characteristics • Variable node geometry – Accuracy variations with space • Sparse/intermittent data • Sub-set detection – Multiple “confidence” levels - Events - Nodes 300 Directivity Conclusions • A system for Localization and Tracking Noise Sources with Autonomous Vehicles has: – been developed – experimentally demonstrated (in a realistic environment). • Next Step: Physically implement low cost nodal processor system. – Implementation on AV’s will further test and improve the systems capabilities.