An MCMC-baesd Particle Filter for Multitarget Tracking within Raw Measurements

Author

Abstract

This paper examines multitarget tracking within raw measurements which has always been considered to be a hassle. This was achieved by separating the state space model of each target into linear and nonlinear subspaces. Then, the Rao–Blackwellization principle was utilized to derive the optimum importance density for special kind of sensor which generates both split and merged measurements within a pixelized observation area. To relieve the complexity associated with the achieved optimum importance, the Gibbs sampler, the well-known sampler from MCMC family, is used to sample from the optimal importance density. The synthetic multitarget tracking scenario using raw data will then be used to show that our new Gibbs sampling method could reach a compromise between accuracy of tracking and computational expense. The proposed idea is motivating to be used in applications where sampling from the optimum proposal density is practically impossible. 

Keywords


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