Automated Model Selection Based Algorithm for Tracking Multiple Nonlinear Trajectories

摘要

Particle filtering is being investigated extensively due to its important feature of target tracking based on nonlinear and non-Gaussian model. It tracks a trajectory with a known model at a given time. It means that particle filter tracks an arbitrary trajectory only if the time instant when trajectory switches from one model to another model is known apriori. Because of this reason particle filter is not able to track any arbitrary tra-jectory where transition from one model to another model is not known. For real world application, trajectory is always random in nature and may follow more than one model. Another problem with multiple trajectories tracking using particle filter is the data association, i.e. observation to track fusion. In this paper we propose a novel method, which overcomes the above problems. In a proposed method an interacting multiple model based approach is used along with particle filtering, which automates the model selection process for tracking an arbitrary trajectory. We have utilized nearest neighbor (NN) method for data association, which is fast and easy to implement.