Labeled random finite sets and the bayes multi-target tracking filter

Ba Ngu Vo, Ba Tuong Vo, Dinh Phung

Research output: Contribution to journalArticleResearchpeer-review

420 Citations (Scopus)

Abstract

An analytic solution to the multi-target Bayes recursion known as the $\delta$-Generalized Labeled Multi-Bernoulli ($\delta$-GLMB) filter has been recently proposed by Vo and Vo in ['Labeled Random Finite Sets and Multi-Object Conjugate Priors,' IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the $\delta$-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the $\delta$-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the \$L1-error in the multi-target density arising from the truncation is presented.

Original language English 6928494 6554-6567 14 IEEE Transactions on Signal Processing 62 24 https://doi.org/10.1109/TSP.2014.2364014 Published - 15 Dec 2014 Yes

Keywords

• Bayesian estimation
• conjugate prior
• marked point process
• random finite set
• target tracking