Tracking of multiple objects via particle filtering faces the difficulty of dealing effectively with high dimensional state spaces. One efficient solution consists of integrating Markov chain Monte Carlo (MCMC) sampling at the core of the particle filter. To accomplish such integration, a few different approaches have been proposed in the literature during the last decade. In this paper, we introduce the derivation of the acceptance probability of the interacting population MCMC particle filter (IP-MCMC-PF), one of the most recent approaches to MCMC-based particle filtering. Additionally, we show that the previous expression known in the literature was incomplete.
|Workshop||Sensor Data Fusion: Trends, Solutions, Applications, SDF 2015|
|Period||6/10/15 → 8/10/15|
- Particle filter