The data-driven delta-generalized labeled multi-Bernoulli tracker for automatic birth initialization

Keith A. LeGrand; Kyle J. DeMars

doi:10.1117/12.2304664

27 April 2018 The data-driven δ-generalized labeled multi-Bernoulli tracker for automatic birth initialization

Keith A. LeGrand, Kyle J. DeMars

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 1064606 (2018) https://doi.org/10.1117/12.2304664
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

The δ-generalized labeled multi-Bernoulli (δ-GLMB) tracker is the first multiple hypothesis tracking (MHT)-like tracker that is provably Bayes-optimal. However, in its basic form, the δ-GLMB provides no mechanism for adaptively initializing targets at their first appearance from unlabeled measurements. By introducing a new multitarget likelihood function that accounts for new target appearance, a data-driven δ-GLMB tracker is derived that automatically initializes new targets in the tracker measurement update. Monte Carlo results of simulated multitarget tracking problems demonstrate improved multitarget tracking accuracy over comparable adaptive birth methods.

Citation Download Citation

Keith A. LeGrand and Kyle J. DeMars "The data-driven δ-generalized labeled multi-Bernoulli tracker for automatic birth initialization", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 1064606 (27 April 2018); https://doi.org/10.1117/12.2304664

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