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Proceedings Paper

Comparison of bias removal algorithms in track-to-track association
Author(s): Shozo Mori; Chee Chong
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Paper Abstract

This paper compares the performance of several algorithms for estimating relative sensor biases when two sets of sensor tracks from two sensor systems are to be fused to form system tracks. The primary focus of this paper is the algorithms' performance, particularly in terms of the mean-square estimation error criterion. The efficiency of the algorithms is not our focus for this study. We are especially interested in three estimation algorithms: (1) the joint track-association/ relative-bias-estimation maximum a posteriori (MAP) probability-density/probability-mass function algorithm; (2) the marginal MAP probability density estimation algorithm; and (3) the minimum-variance (MV) estimation algorithm. Those algorithms rely on the capability of generating and evaluating multiple significant track-to-track association hypotheses, which may be obtained by any of the recently developed k-best bipartite data assignment algorithms. Several other algorithms that have been considered in the past will also be discussed.

Paper Details

Date Published: 21 September 2007
PDF: 9 pages
Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 66990T (21 September 2007); doi: 10.1117/12.735383
Show Author Affiliations
Shozo Mori, BAE Systems, Advanced Information Technologies (United States)
Chee Chong, BAE Systems, Advanced Information Technologies (United States)


Published in SPIE Proceedings Vol. 6699:
Signal and Data Processing of Small Targets 2007
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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