Robust neural-net-based data association and multiple model-based tracking of multiple point targets

Mukesh A. Zaveri; Uday B. Desai; Shabbir N. Merchant

doi:10.1117/12.521177

28 May 2004 Robust neural-net-based data association and multiple model-based tracking of multiple point targets

Mukesh A. Zaveri, Uday B. Desai, Shabbir N. Merchant

Proceedings Volume 5298, Image Processing: Algorithms and Systems III; (2004) https://doi.org/10.1117/12.521177
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment without using apriori information about the target dynamic. We propose a neural network based tracking algorithm, incorporating interacting multiple model to track both maneuvering and non-maneuvering targets simultaneously in the presence of dense clutter. For data association, we use the Expectation-Maximization (EM) algorithm and Hopfield network to evaluate assignment weights. All validated measurements are used to update the target state and hence, it avoids the uncertainty about the origin of the measurements. In the proposed approach the data association process is defined to incorporate multiple models for target dynamics and probability density function (pdf) of an observed data given target state and measurement association, is treated as a mixture pdf. This allows to combine the likelihood of a measurement due to each model, and consequently, it is possible to track any arbitrary trajectory in the presence of dense clutter.

Citation Download Citation

Mukesh A. Zaveri, Uday B. Desai, and Shabbir N. Merchant "Robust neural-net-based data association and multiple model-based tracking of multiple point targets", Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); https://doi.org/10.1117/12.521177

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