The Marginal Enumeration Bayesian Cramer-Rao Bound for Jump Markov Systems

A marginal version of the enumeration Bayesian Cramer-Rao Bound (EBCRB) for jump Markov systems is proposed. It is shown that the proposed bound is at least as tight as EBCRB and the improvement stems from better handling of the nonlinearities. The new bound is illustrated to yield tighter results than BCRB and EBCRB on a benchmark example

New Prediction for Extended Targets With Random Matrices

This paper presents a new prediction update for extended targets whose extensions are modeled as random matrices. The prediction is based on several minimizations of the Kullback-Leibler divergence (KL-div) and allows for a kinematic state dependent transformation of the target extension. The results show that the extension prediction is a significant improvement over the previous work carried out on the topic.Funding Agencies|Linnaeus research environment CADICS - Swedish Research Council; frame project grant Extended Target Tracking - Swedish Research Council [621-2010-4301]; Collaborative Unmanned Aircraft Systems (CUAS) - Swedish Foundation for Strategic Research (SSF)</p