A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation

Ba Tuong Vo; Ba Ngu Vo

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arXiv · 1805.10038

A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation

Ba Tuong Vo·Ba Ngu Vo

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Abstract

State space models in which the system state is a finite set--called the multi-object state--have generated considerable interest in recent years. Smoothing for state space models provides better estimation performance than filtering by using the full posterior rather than the filtering density. In multi-object state estimation, the Bayes multi-object filtering recursion admits an analytic solution known as the Generalized Labeled Multi-Bernoulli (GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate the multi-object posterior. We also propose an implementation of this so-called multi-scan GLMB posterior recursion using a similar approach to the GLMB filter implementation.

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BibTeX RIS

Ba Tuong Vo, Ba Ngu Vo. 2018-05-25. A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation. https://arxiv.org/abs/1805.10038

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