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A Scalable Real-Time Multiple-Target Tracking
Algorithm for Sensor Networks
Songhwai Oh, Luca Schenato, Phoebus Chen, and Shankar Sastry
Multiple-target tracking is a representative real-time application of
sensor networks as it exhibits different aspects of sensor networks
such as event detection, sensor information fusion, multi-hop
communication, sensor management and real-time decision making. The
task of tracking multiple objects in a sensor network is challenging
due to constraints on a sensor node such as short communication and
sensing ranges, a limited amount of memory and limited computational
power. In addition, since a sensor network surveillance system needs
to operate autonomously without human operators, it requires an
autonomous real-time tracking algorithm which can track an unknown
number of targets. In this paper, we develop a scalable real-time
multiple-target tracking algorithm that is autonomous and robust
against transmission failures, communication delays and sensor
localization error. In particular, there is no performance loss up to
the average localization error of .7 times the separation between
sensors and the algorithm tolerates up to 50% lost-to-total packet
ratio and 90% delayed-to-total packet ratio.
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