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Multiple-target tracking and identity management

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6 Author(s)
Inseok Hwang ; Dept. of Aeronaut. & Astronaut., Stanford Univ., CA, USA ; Balakrishnan, H. ; Roy, K. ; Jaewon Shin
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This paper involves the development of an algorithm which can simultaneously track and manage identities of multiple targets in a sensor network, for the purpose of air traffic control. We propose a logical integration of joint probabilistic data association (JPDA) (Bar-Shalom and Fortmann, 1988), used for associating measurements with targets, and the identity management (IM) (Shin et al., 2003) algorithm for sensor networks, which utilizes target attribute information from local sensors to maintain the target's identity correctly. For target tracking, we use a modified version of the interacting multiple model (IMM) algorithm called the residual-mean IMM (RMIMM) which we developed (Hwang et al., 2003). The proposed algorithm incorporates target state estimate information from the tracking algorithm into the evolution of a doubly-stochastic belief matrix for the target identities, and also assimilates any local information available. The algorithm has been shown not only to converge, but also to not increase the uncertainty in our belief.

Published in:

Sensors, 2003. Proceedings of IEEE  (Volume:1 )

Date of Conference:

22-24 Oct. 2003

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