21 September 2007 Comparison of bias removal algorithms in track-to-track association
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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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shozo Mori and Chee Chong "Comparison of bias removal algorithms in track-to-track association", Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 66990T (21 September 2007); doi: 10.1117/12.735383; https://doi.org/10.1117/12.735383


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