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Proceedings Paper

Probabilistic data association in high clutter environments
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Paper Abstract

Data association is the key component in single or multiple target tracking algorithms with measurement origin. Probabilistic Data Association (PDA), in which all validated measurements are associated probabilistically to the predicted estimate, is a well-known method to handle the measurement origin uncertainty. In PDA, the effect of measurement origin uncertainty is incorporated into the updated covariance by adding the spread of the innovations term. The updated covariance may become very large after few time steps in high clutter scenarios due to spread of the innovations term. Large covariance results in a large gate, which is used to limit the possible measurements that could have originated from the target. Hence, the track will be lost and estimate will just follow the prediction. Also, large gate will make the well-separated target assumption invalid, even if the targets are well-separated. Hence, after a few time steps all the targets in the surveillance region come under the same group, making the Joint Probabilistic Data Association (JPDA). In this paper, adaptive gating techniques are proposed to avoid the steady increase in the updated covariance in high clutter. The effectiveness of the proposed techniques is demonstrated on simulated data.

Paper Details

Date Published: 15 April 2010
PDF: 10 pages
Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 76980L (15 April 2010); doi: 10.1117/12.851070
Show Author Affiliations
R. Tharmarasa, McMaster Univ. (Canada)
T. Lang, General Dynamics Canada (Canada)
Mike McDonald, Defence Research and Development Canada (Canada)
T. Kirubarajan, McMaster Univ. (Canada)

Published in SPIE Proceedings Vol. 7698:
Signal and Data Processing of Small Targets 2010
Oliver E. Drummond, Editor(s)

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