Dynamic Agent Classification and Tracking Using an Ad Hoc Mobile Acoustic Sensor Network
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Dynamic Agent Classification and Tracking Using an Ad Hoc Mobile Acoustic Sensor Network David Friedlander Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA 16801-0030, USA Email: [email protected]
Christopher Griffin Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA 16801-0030, USA Email: cgriffi[email protected]
Noah Jacobson Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA 16801-0030, USA Email: [email protected]
Shashi Phoha Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA 16801-0030, USA Email: [email protected]
Richard R. Brooks Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA 16801-0030, USA Email: [email protected] Received 12 December 2001 and in revised form 5 October 2002 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. Keywords and phrases: sensor networks, distributed computing, target tracking, target identification, self-organizing systems.
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INTRODUCTION
Distributed sensing systems combine observations from a large area network of sensors, creating the need for platform self-organization and the sharing of sensor information between platforms. It is difficult to integrate the data from each sensor into a single context for the entire network. Instead, groups of sensors in local areas collaborate to produce useful information to the end user.
Our objective is to create a distributed wireless network of sensors covering large areas to obtain an accurate representation of dynamic processes occurring within the region. Such networks are subject t
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