A clutter-agnostic generalized labeled multi-Bernoulli filter

Ronald Mahler

doi:10.1117/12.2305464

27 April 2018 A clutter-agnostic generalized labeled multi-Bernoulli filter

Ronald Mahler

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 106460D (2018) https://doi.org/10.1117/12.2305464
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

The labeled random finite set (LRFS) theory of B.-T. Vo and B.-N. Vo is the first systematic, theoretically rigorous formulation of true multitarget tracking, and is the basis for the generalized labeled multi-Bernoulli (GLMB) filter (the first implementable and provably Bayes-optimal multitarget tracking algorithm). Like most multitarget trackers, the GLMB filter is based on the assumption that clutter statistics are known a priori. Recent research has introduced RFS filters that are "clutter-agnostic," in the sense that they can address unknown, dynamically evolving clutter. These filters were unlabeled, however. In this paper we devise a clutter-agnostic GLMB (CA-GLMB) filter, based on the Bernoulli clutter-generator concept.

Conference Presentation

Citation Download Citation

Ronald Mahler "A clutter-agnostic generalized labeled multi-Bernoulli filter", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460D (27 April 2018); https://doi.org/10.1117/12.2305464

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