Concurrent MAP data association and absolute bias estimation with an arbitrary number of sensors
Bret D. Kragel, Scott Danford, Aubrey B. Poore · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
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.