A generalized labeled multi-Bernoulli filter for correlated multitarget systems

Ronald Mahler

doi:10.1117/12.2305463

27 April 2018 A generalized labeled multi-Bernoulli filter for correlated multitarget systems

Ronald Mahler

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 106460C (2018) https://doi.org/10.1117/12.2305463
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). Several of the author’s earlier papers investigated Bayes filters that propagate the correlations between two unlabeled evolving multitarget systems—but with limited success. In this paper we provide a theoretically rigorous and much more general approach, by devising a GLMB filter that propagates the correlations between two evolving labeled multitarget systems.

Conference Presentation

Citation Download Citation

Ronald Mahler "A generalized labeled multi-Bernoulli filter for correlated multitarget systems", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460C (27 April 2018); https://doi.org/10.1117/12.2305463

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