Bayesian Multi-tracking with Superpositional Measurements using Labeled Random Finite Sets

Papi, F and Kim, D 2015, 'Bayesian Multi-tracking with Superpositional Measurements using Labeled Random Finite Sets', in Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015), Nice, France, 31 August - 4 September 2015, pp. 2211-2215.


Document type: Conference Paper
Collection: Conference Papers

Title Bayesian Multi-tracking with Superpositional Measurements using Labeled Random Finite Sets
Author(s) Papi, F
Kim, D
Year 2015
Conference name EUSIPCO 2015
Conference location Nice, France
Conference dates 31 August - 4 September 2015
Proceedings title Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015)
Publisher IEEE
Place of publication United States
Start page 2211
End page 2215
Total pages 5
Abstract In this paper we present a general solution for multi-target tracking problems with superpositional measurements. In a superpositional sensor model, the measurement collected by the sensor at each time step is a superposition of measurements generated by each of the targets present in the surveillance area. We use the Bayes multi-target filter with Labeled Random Finite Set (RFS) in order to jointly estimate the number of targets and their trajectories. We propose an implementation of this filter using Sequential Monte Carlo (SMC) methods with an efficient multi-target sampling strategy based on the Approximate Superpositional Cardinalized Probability Hypothesis Density (CPHD) filter.
Subjects Signal Processing
DOI - identifier 10.1109/EUSIPCO.2015.7362777
Copyright notice © 2015 IEEE
ISBN 9780992862633
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