An efficient particle filter for multi-target tracking using an independence assumption

The particle filter (PF) based multi-target tracking (MTT) methods suffer from the curse of dimensionality. Existing strategies to combat this assume posterior independence between target states, in order to then sample targets independently, or perform joint sampling of closely spaced targets only. When many targets are in proximity, these strategies either perform poorly or are too computationally expensive. We make two contributions towards addressing these limitations. Firstly, we advocate an alternative view of the use of posterior independence which emphasises the statistical effect of assuming posterior independence on the Monte Carlo (MC) approximation of posterior density. Our analysis suggests that assuming posterior independence can obtain a better MC approximation of the prior distribution without regard for how sampling is performed. Secondly, we present a computationally efficient joint sampling method to cope with the measurement ambiguity when targets are near each other. Consequently, we develop a PF which employs posterior independence while sampling targets jointly. Simulation results for a challenging tracking scenario show that the proposed PF substantially outperforms existing approaches.