Conference Paper

Tracking moving targets in a smart sensor network

Dept. of ECECS, Cincinnati Univ., OH, USA
DOI: 10.1109/VETECF.2003.1286181 Conference: Vehicular Technology Conference, 2003. VTC 2003-Fall. 2003 IEEE 58th, Volume: 5
Source: IEEE Xplore

ABSTRACT Networks of small, densely distributed wireless sensor nodes are capable of solving a variety of collaborative problems such as monitoring and surveillance. We develop a simple algorithm that detects and tracks a moving target, and alerts sensor nodes along the projected path of the target. The algorithm involves only simple computation and localizes communication only to the nodes in the vicinity of the target and its projected course. The algorithm is evaluated on a small-scale testbed of Berkeley motes using a light source as the moving target. The performance results are presented emphasizing the accuracy of the technique, along with a discussion about our experience in using such a platform for target tracking experiments.

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    ABSTRACT: Binary proximity sensors (BPS) provide extremely low cost and privacy preserving features for tracking mobile targets in smart environment, but great challenges are posed for track- ing multiple targets, because a BPS cannot distinguish one or multiple targets are in its sensing range. In this paper, we at first address the counting problem by presenting a maxi- mum clique partition model on unit disk graph, which leads to a tight lower bound for estimating the number of targets by a snapshot of sensor readings. Then, to more accurately count and track the multiple targets by sequential readings of sensors, we state the key is to comprehensively infer the states behind the events. Therefore, at each event we infer which target may trigger the event via a dynamic coloring technique (DEC) and predict the potential regions of the multiple targets by a colorful area shrinking and expanding approach. Such an approach generates multiple potential scenarios containing different colors to interpret the sequen- tial events, where the number of colors indicates the different estimations of the target number. Then we designed multi- color particle filter (MCPF), which is run in parallel in each scenario to enumerate and evaluate the potential trajecto- ries of the targets under the color constraint. The likelihoods of the trajectories are evaluated by each target's movement consistence. The overall best trajectory over all scenarios is voted to provide not only the most possible target number, but also the trajectories of the targets. Extensive simula- tions were conducted using a multi-agent simulator which show good accuracy of the proposed multi-target tracking algorithms.
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