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A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking
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DOI：10.3390/s19194226.png)
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Abstract
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A forward-backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed under LMB prior. It is also shown that the proposed LMB smoother can improve both the cardinality estimation and the state estimation, and the major computational complexity is linear with the number of targets. Implementation based on the Sequential Monte Carlo method in a representative scenario has demonstrated the effectiveness and computational efficiency of the proposed smoother in comparison to existing approaches.
Keywords:
random finite set
bayes smoother
labeled multi-Bernoulli
multi-target tracking
Sequential Monte Carlo
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