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Proceedings Paper

The probability of misassociation between neighboring targets
Author(s): Javier A. Areta; Yaakov Bar-Shalom; Ronald Rothrock
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Paper Abstract

This paper presents procedures to calculate the probability that the measurement originating from an extraneous target will be (mis)associated with a target of interest for the cases of Nearest Neighbor and Global association. It is shown that these misassociation probabilities depend, under certain assumptions, on a particular - covariance weighted - norm of the difference between the targets' predicted measurements. For the Nearest Neighbor association, the exact solution, obtained for the case of equal innovation covariances, is based on a noncentral chi-square distribution. An approximate solution is also presented for the case of unequal innovation covariances. For the Global case an approximation is presented for the case of "similar" innovation covariances. In the general case of unequal innovation covariances where this approximation fails, an exact method based on the inversion of the characteristic function is presented. The theoretical results, confirmed by Monte Carlo simulations, quantify the benefit of Global vs. Nearest Neighbor association. These results are applied to problems of single sensor as well as centralized fusion architecture multiple sensor tracking.

Paper Details

Date Published: 16 April 2008
PDF: 11 pages
Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 69691B (16 April 2008); doi: 10.1117/12.778075
Show Author Affiliations
Javier A. Areta, Univ. of Connecticut (United States)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)
Ronald Rothrock, SPARTA, Inc. (United States)

Published in SPIE Proceedings Vol. 6969:
Signal and Data Processing of Small Targets 2008
Oliver E. Drummond, Editor(s)

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