Autonomous networks of sensor platforms can be designed to interact in dynamic and noisy environments to determine the occurrence of specified transient events that define the dynamic process of interest. For example, a sensor network may be used for battlefield surveillance with the purpose of detecting, identifying, and tracking enemy activity. When the number of nodes is large, human oversight and control of low-level operations is not feasible. Coordination and self-organization of multiple autonomous nodes is necessary to maintain connectivity and sensor coverage and to combine information for better understanding the dynamics of the environment. Resource conservation requires adaptive clustering in the vicinity of the event. This paper presents methods for dynamic distributed signal processing using an ad hoc mobile network of microsensors to detect, identify, and track targets in noisy environments. They seamlessly integrate data from fixed and mobile platforms and dynamically organize platforms into clusters to process local data along the trajectory of the targets. Local analysis of sensor data is used to determine a set of target attribute values and classify the target. Sensor data from a field test in the Marine base at Twentynine Palms, Calif, was analyzed using the techniques described in this paper. The results were compared to "ground truth" data obtained from GPS receivers on the vehicles.
This article is part of the series Sensor Networks.
Dynamic Agent Classification and Tracking Using an Ad Hoc Mobile Acoustic Sensor Network
Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA 16801-0030, USA
EURASIP Journal on Advances in Signal Processing 2003, 2003:819145 doi:10.1155/S1110865703212099
The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2003/4/819145
|Received:||12 December 2001|
|Revisions received:||5 October 2002|
|Published:||30 March 2003|
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