Record Details


Field	Value

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

Names	ZAVERI, MA DESAI, UB MERCHANT, SN
Date Issued	2004 (iso8601)
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.
Genre	Proceedings Paper
Topic	Expectation-Maximization Algorithm
Identifier	IMAGE PROCESSING: ALGORITHMS AND SYSTEMS III,5298,337-348

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