Title: Multi-target Track-Before-Detect using Labeled Random Finite Set
Authors: PAPI FRANCESCOVO Ba-TuongBOCQUEL MélanieVO Ba-Ngu
Citation: 2013 International Conference on Control, Automation and Information Sciences (ICCAIS) p. 116-121
Publisher: IEEE
Publication Year: 2013
JRC N°: JRC84786
ISBN: 978-1-4799-0572-0
URI: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6720540
http://publications.jrc.ec.europa.eu/repository/handle/JRC84786
Type: Articles in periodicals and books
Abstract: Multi-target tracking requires the joint estimation of the number of target trajectories and their states from a sequence of observations. In low signal-to-noise ratio (SNR) scenarios, the poor detection probability and large number of false observations can greatly degrade the tracking performance. In this case an approach called Track-Before-Detect (TBD) that operates on the pre-detection signal, is needed. In this paper we present a labeled random finite set solution to the multi-target TBD problem. To the best of our knowledge this is the first provably Bayes optimal approach to multi-target tracking using image data. Simulation results using realistic radar-based TBD scenarios are also presented to demonstrate the capability of the proposed approach.
JRC Directorate:Space, Security and Migration

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