Multitarget State Extraction for the PHD Filter using MCMC Approach
Weifeng Liu, Chongzhao Han, Feng Lian, Hongyan Zhu · IEEE Transactions on Aerospace and Electronic Systems · 2010
It is known that multitarget states cannot be directly derived from the particle probability hypothesis density (particle-PHD) filter. Therefore, some cluster algorithms are used to extract the states from the particles. Actually, these algorithms become a crucial step in how to cluster the particles effectively and robustly in the particle-PHD filter. A novel multitarget state extraction algorithm for the particle-PHD filter is proposed. The proposed algorithm is comprised of two steps. First, the target number is calculated via the particle-PHD filter. Second, the distribution of the particles is fitted using finite mixture models (FMMs), whose parameters can be derived using a Markov chain Monte Carlo (MCMC) sampling scheme. Then the states can be extracted according to the fitted mixture distribution. The final simulations show that the proposed algorithm is effective for the extraction of the individual states even when the clutter is dense and the distribution of the particles is relatively complex.