Multisensor fusion using homotopy particle filter

Homotopy particle filters (HPFs), recently developed by Daum and Huang [1], present an alternative nonlinear filtering approach to sampling-based particle filters. Homotopy filters perform information update using the flow of particles to regions with high measurement likelihood. The particle flows in HPFs are solutions to a Fokker-Plank equation governing the dynamics of the posterior density function. The resulting partial differential equation is highly under-determined and has many solutions. In this work we study the nonzero-diffusion flow, and show its advantage in multisensor fusion. The nonzero-diffusion flow was chosen because it has the form of an information filter (inverse-covariance filter), and this feature makes it suitable for integration of measurement contributions from different sensors. The effectiveness of the HPF with nonzero diffusion flow as a fusion mechanism was evaluated for tracking a moving target using multiple range and bearing sensors. It is shown that the HPF requires several orders of magnitude fewer particles than a sampling-based particle filter, without loss in state estimator performance.