Multi-Target State Extraction for the SMC-PHD Filter

Abstract

The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been demonstrated to be a favorable method for multi-target tracking. However, the time-varying target states need to be extracted from the particle approximation of the posterior PHD, which is difficult to implement due to the unknown relations between the large amount of particles and the PHD peaks representing potential target locations. To address this problem, a novel multi-target state extraction algorithm is proposed in this paper. By exploiting the information of measurements and particle likelihoods in the filtering stage, we propose a validation mechanism which aims at selecting effective measurements and particles corresponding to detected targets. Subsequently, the state estimates of the detected and undetected targets are performed separately: the former are obtained from the particle clusters directed by effective measurements, while the latter are obtained from the particles corresponding to undetected targets via clustering method. Simulation results demonstrate that the proposed method yields better estimation accuracy and reliability compared to existing methods.

Tracking Ground Targets with a Road Constraint Using a GMPHD Filter

“…Even so, it still involves multidimensional integration, that must be approximated using some implementation method. Generally, the PHD can be implemented by using both Gaussian mixing and sequential Monte Carlo approximation techniques, because in principle, the sequence Monte Carlo (SMC) implementation of the PHD filter [ 25 , 26 , 27 , 28 ] can adapt to any Markov target dynamic model. Nevertheless, the SMC method has some inherent flaws, that is, it requires many particles and there is unreliability in the clustering algorithm for state extraction [ 26 , 29 ].…”

Tracking the Turn Maneuvering Target Using the Multi-Target Bayes Filter with an Adaptive Estimation of Turn Rate

“…Target tracking has been discussed in many articles due to its military and civil applications, which range from threat warnings, to intelligent surveillance and situational awareness [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Maneuvering target tracking is the most essential ingredient of target tracking and has attracted the attention of many researchers.…”