Advanced IP-MCMC-PF design ingredients

This paper proposes techniques to improve the properties of Sequential Markov Chain Monte Carlo (SMCMC) methods in the context of multi-target tracking. In particular, we extend the Interacting Population-based MCMC Particle Filter (IP-MCMC-PF) with three different methods: delayed rejection, genetic algorithms, and simulated annealing. Each of these methods furnishes the IP-MCMC-PF algorithm with different theoretical guarantees which are empirically analysed in this paper. Firstly, the use of delayed rejection in the Metropolis-Hastings (MH) samplers is proposed in order to reduce the asymptotic variance of the estimate. Secondly, the crossover operator, inspired by genetic algorithms, is presented as a mechanism to increase the interaction of the MH samplers. Thus, attaining fast convergence of the time-consuming MCMC step. Thirdly, simulated annealing is introduced with the goal of increasing the robustness of the algorithm against divergence due to e.g. poor initialisations. Finally, the results from our experiments show that the proposed methods strengthen the multi-target tracker in the aforementioned aspects.