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Proceedings Paper

Concurrent MAP data association and absolute bias estimation with an arbitrary number of sensors
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Paper Abstract

Bias estimation using objects with unknown data association requires concurrent estimation of both biases and optimal data association. This report derives maximum a posteriori (MAP) data association likelihood ratios for concurrent bias estimation and data association based on sensor-level track state estimates and their joint error covariance. Our approach is unique for two reasons. First, we include a bias prior that allows estimation of absolute sensor biases, rather than just relative biases. Second, we allow concurrent bias estimation and association for an arbitrary number of sensors. The two-sensor likelihood ratio is derived as a special case of the general M-sensor result.

Paper Details

Date Published: 16 April 2008
PDF: 11 pages
Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 69691G (16 April 2008); doi: 10.1117/12.784375
Show Author Affiliations
Bret D. Kragel, Numerica Corp. (United States)
Scott Danford, Numerica Corp. (United States)
Aubrey B. Poore, Numerica Corp. (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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