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|Title:||Multi-target Track-Before-Detect using Labeled Random Finite Set|
|Authors:||PAPI FRANCESCO; VO Ba-Tuong; BOCQUEL Mélanie; VO Ba-Ngu|
|Citation:||2013 International Conference on Control, Automation and Information Sciences (ICCAIS) p. 116-121|
|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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