Comparison of bias removal algorithms in track-to-track association

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