Automated model selection based tracking of multiple targets using particle filtering

Particle filtering is being investigated extensively due to its important feature of target tracking based on nonlinear and non-Gaussian models. It tracks a trajectory with a known model at a given time. It means that the particle filter tracks an arbitrary trajectory only if the time instant when the trajectory switches from one model to another model is known a priori. For this reason, a particle filter is not able to track any arbitrary trajectory where the transition instant from one model to another model is not known. Another problem with multiple trajectory tracking using particle filters is the data association, i.e. observation to track fusion. We propose a novel method, which overcomes both the above problems. An interacting multiple model based approach is used along with particle filtering, which automates the model selection process for tracking an arbitrary trajectory. The uncertainty about the origin of an observation is overcome by using a centroid of measurements to evaluate weights for particles as well as to calculate the likelihood of a model.