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16 April 2008 Concurrent MAP data association and absolute bias estimation with an arbitrary number of sensors
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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.
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Bret D. Kragel, Scott Danford, and Aubrey B. Poore "Concurrent MAP data association and absolute bias estimation with an arbitrary number of sensors", Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 69691G (16 April 2008); https://doi.org/10.1117/12.784375
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