Multi-target Track-Before-Detect using Labeled Random Finite Set

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,