Netted Sensors-based Vehicle Acoustic Classification at Tier 1 Nodes
December 2005
Garry M. Jacyna, The MITRE Corporation
Carol T. Christou, The MITRE Corporation
E. Bryan George, The MITRE Corporation
Burhan F. Necioglu, The MITRE Corporation
ABSTRACT
The MITRE Corporation has embarked on a three-year internally-funded research program in netted sensors with applications
to border monitoring, situational awareness in support of combat identification, and urban warfare. The first-year
effort emphasized a border monitoring application for dismounted personnel and vehicle surveillance. This paper will
focus primarily on the Tier 1 acoustic-based vehicle classification component. In a hierarchical network topology, distributed
clusters of less capable nodes (Tier 1) at the lowest layer are in communication with more capable super nodes (Tier
2) at the next highest layer. Specifically, coarse information is aggregated at the lowest layer and communicated to the
next layer in the network where more refined processing and information extraction can take place. We determined that
fairly sophisticated classification processing can be performed at a Tier 1 node. This results in sizeable energy savings
since data transfer rates are reduced across and between network layers. In general, acoustic-based vehicle classification
is a difficult signal processing problem independent of the considerable hardware challenges. The acoustic waveforms are
highly non-stationary and lack discernible harmonic structure, particularly for commercial vehicles that are designed to be
intentionally quiet. In addition, the waveforms are rather complicated functions of the vehicle speed, engine RPM rate, and
sensor-to-vehicle aspect angle. We discuss the development and implementation of a robust linear-weighted classifier on
a Mica2 Crossbow mote using feature extraction algorithms specifically developed by MITRE for mote-based processing
applications. These include a block floating point Fast Fourier Transform (FFT) algorithm and an 8-band proportional
bandwidth filter bank. Results of in-field testing are compared and contrasted with theoretically-derived performance
bounds.
Additional Search Keywords
acoustics, feature extraction, linear discrimination, distributed classification, Mica2 Crossbow motes
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