Computer Science – Systems and Control
Scientific paper
2012-03-14
Computer Science
Systems and Control
Submitted to 15th International Conference on Information Fusion (2012)
Scientific paper
Random finite sets (RFSs) has been a fruitful area of research in recent years, yielding new approximate filters such as the probability hypothesis density (PHD), cardinalised PHD (CPHD), and multiple target multi-Bernoulli (MeMBer). These new methods have largely been based on approximations that side-step the need for measurement-to-track association in order to maintain tractability. Due to their relative intractability, methods that incorporate data association have received little attention. This paper provides a RFS algorithm that incorporates data association, but retains computational tractability via a recently developed, high quality approximation of marginal association probabilities. A derivation of the full Bayes RFS filter is provided, demonstrating a conjugate prior for commonly invoked assumptions. Different approximations are applied in order to obtain tractable algorithms, which maintain a multi-Bernoulli representation. The methods proposed include the marginal track filter (MTF), which is a natural extension of the joint target detection and tracking (JoTT) filter to multiple targets, and a variant of the MeMBer filter which retains its structure, but utilises the approximate marginal association weights. A solution to the coalescence issues of the MTF is proposed, and promising performance is demonstrated in a challenging scenario. This extended version incorporates proofs of two results not included in the main paper due to space limitations.
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