On CPHD filters with track labeling
Access Status
Authors
Date
2017Type
Metadata
Show full item recordCitation
Source Title
ISBN
School
Collection
Abstract
© 2017 SPIE. The random infinite set (RFS) approach to information fusion addressed target track-labeling from the outset. The first implementations of RFS filters did not do so because of computational concerns, whereas subsequent implementations employed heuristics. The labeled RFS (LRFS) theory of B.-T. Vo and B.-N. Vo was the first systematic, theoretically rigorous formulation of true multitarget tracking; and led to the generalized labeled multi-Bernoulli (GLMB) filter (the first provably Bayes-optimal multitarget tracking algorithm). This paper addresses the feasibility of theoretically rigorous cardinalized probability hypothesis density (CPHD) filters. We show that an approximation of the GLMB filter, known as the LMB filter, can be reinterpreted as a theoretically rigorous labeled PHD (LPHD) filter. We also prove two characterization theorems for the probability generating functionals (p.g.fl's) of LRFS's.
Related items
Showing items related by title, author, creator and subject.
-
Nguyen, Tran Thien Dat ; Kim, Du Yong (2019)In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple ...
-
Reuter, S.; Vo, Ba Tuong; Vo, Ba-Ngu; Dietmayer, K. (2014)This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias ...
-
Reuter, S.; Vo, Ba Tuong; Vo, Ba-Ngu; Dietmayer, K. (2014)In this paper, we propose the labeled multi-Bernoulli filter which explicitly estimates target tracks and provides a more accurate approximation of the multi-object Bayes update than the multi-Bernoulli filter. In particular, ...