3 research outputs found
A track-before-detect labelled multi-Bernoulli particle filter with label switching
This paper presents a multitarget tracking particle filter (PF) for general
track-before-detect measurement models. The PF is presented in the random
finite set framework and uses a labelled multi-Bernoulli approximation. We also
present a label switching improvement algorithm based on Markov chain Monte
Carlo that is expected to increase filter performance if targets get in close
proximity for a sufficiently long time. The PF is tested in two challenging
numerical examples.Comment: Accepted for publication in IEEE Transactions on Aerospace and
Electronic System
Computationally-tractable approximate PHD and CPHD filters for superpositional sensors
In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle filter approximations. As an example, we present simulation experiments that involve tracking multiple targets using acoustic amplitude sensors and a radio-frequency tomography sensor system. Our simulation study indicates that the CPHD filter provides promising tracking accuracy with reasonable computational requirements. © 2007-2012 IEEE