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In this paper, we propose a strategy that is based on expectation maximization for tracking multiple point targets. The algorithm is similar to probabilistic multi-hypothesis tracking (PMHT) but does not relax the point target model assumptions. According to the point target models, a target can generate at most one measurement, and a measurement is generated by at most one target. With this model...
In this paper, we address the problem of smoothing on Gaussian mixture (GM) posterior densities using the two-filter smoothing (TFS) strategy. The structure of the likelihoods in the backward filter of the TFS is analysed in detail. These likelihoods look similar to GMs, but are not proper density functions in the state-space since they may have constant value in a subspace of the state space. We...
Conventional forward-backward smoothing (FBS) for Gaussian mixture (GM) problems are based on pruning methods which yield a degenerate hypothesis tree and often lead to underestimated uncertainties. To overcome these shortcomings, we propose an algorithm that is based on merging components in the GM during filtering and smoothing. Compared to FBS based on the N-scan pruning, the proposed algorithm...
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