Particle filtering is investigated extensively due to its importance in target tracking for nonlinear and non-Gaussian models. A particle filter can track an arbitrary trajectory only if the target dynamics models are known and the time instant when trajectory switches from one model to another model is known a priori. In real applications, it is unlikely to meet both these conditions. We propose a novel method that overcomes the lack of this knowledge. In the proposed method, an interacting multiple-model-based approach is exploited along with particle filtering. Moreover, we automate the model selection process for tracking an arbitrary trajectory. In the proposed approach, a priori information about the exact model that a target may follow is not required. Another problem with multiple trajectory tracking using a particle filter is data association, namely, observation to track fusion. For data association, we use three methods. In the first case, an implicit observation to track assignment is performed using a nearest neighbor (NN) method for data association; this is fast and easy to implement. In the second method, 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. In the third method, a Markov random field (MRF)-based method is used. The MRF method enables us to exploit the neighborhood concept for data association, i.e., the association of a measurement influences an association of its neighboring measurement.