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
trajectory 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.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.