Radar target tracking in cluttered environment based on particle filtering

This paper deals with the problem of radar target tracking in cluttered environment from plane position indicator (PPI) radar images collected by low-cost incoherent radar. For this purpose a new five-step technique is proposed, including background subtraction, clutter suppression, measurements extraction, tracking and data fusion; the tracking step uses a particle filtering based data association method. Radar measurements, including target information and clutter interference, are checked whether it belongs to tracking target by data association with Kalman predicted state. If the measurement is generated by target, target state is updated by Kalman filter, and vice versa the predicted state keeps invariant. Moreover, smoothed tracks are given by Kalman smoothing of filtering results. The performance of the tracking algorithm is deeply investigated against Monte Carlo simulations. Finally, the overall multi-frame-based technique is applied to two sets of live PPI radar images, and the results show the effectiveness of the proposed approach.