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.
PAPI Francesco;
VO Ba-Tuong;
BOCQUEL Mélanie;
VO Ba-Ngu;
2014-11-17
IEEE
JRC84786
978-1-4799-0572-0,
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6720540,
https://publications.jrc.ec.europa.eu/repository/handle/JRC84786,
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