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Proceedings Paper

A stochastic grid filter for multi-target tracking
Author(s): Surrey Kim; Michael Alexander Kouritzin; Hongwei Long; Jesse Daniel McCrosky; Xingqiu Zhao
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Paper Abstract

In this paper, we discuss multi-target tracking for a submarine model based on incomplete observations. The submarine model is a weakly interacting stochastic dynamic system with several submarines in the underlying region. Observations are obtained at discrete times from a number of sonobuoys equipped with hydrophones and consist of a nonlinear function of the current locations of submarines corrupted by additive noise. We use filtering methods to find the best estimation for the locations of the submarines. Our signal is a measure-valued process, resulting in filtering equations that can not be readily implemented. We develop Markov chain approximation approach to solve the filtering equation for our model. Our Markov chains are constructed by dividing the multi-target state space into cells, evolving particles in these cells, and employing a random time change approach. These approximations converge to the unnormalized conditional distribution of the signal process based on the back observations. Finally we present some simulation results by using the refining stochastic grid (REST) filter (developed from our Markov chain approximation method).

Paper Details

Date Published: 9 August 2004
PDF: 9 pages
Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.546125
Show Author Affiliations
Surrey Kim, Univ. of Alberta (Canada)
Michael Alexander Kouritzin, Univ. of Alberta (Canada)
Hongwei Long, Univ. of Alberta (Canada)
Jesse Daniel McCrosky, Univ. of Alberta (Canada)
Xingqiu Zhao, Univ. of Alberta (Canada)


Published in SPIE Proceedings Vol. 5429:
Signal Processing, Sensor Fusion, and Target Recognition XIII
Ivan Kadar, Editor(s)

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