Share Email Print

Proceedings Paper

Robust neural-net-based data association and multiple model-based tracking of multiple point targets
Author(s): Mukesh A. Zaveri; Uday B. Desai; Shabbir N. Merchant
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 28 May 2004
PDF: 12 pages
Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); doi: 10.1117/12.521177
Show Author Affiliations
Mukesh A. Zaveri, Indian Institute of Technology-Bombay (India)
Uday B. Desai, Indian Institute of Technology-Bombay (India)
Shabbir N. Merchant, Indian Institute of Technology-Bombay (India)

Published in SPIE Proceedings Vol. 5298:
Image Processing: Algorithms and Systems III
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

© SPIE. Terms of Use
Back to Top