A novel track initialization algorithm based on random sample consensus in dense clutter

Track initialization in dense clutter environments is an important topic and still a challenging task. Most traditional track initialization techniques firstly consider all possible associated measurement combinations and select the optimal one as an initialized track. Therefore, dense clutter brings great challenges to traditional algorithms. Random sample consensus algorithm, which is different from traditional algorithms, starts from minimum measurements. It samples randomly minimum measurements to establish hypotheses and verifies them through remaining measurements. However, the randomness of sampling influences algorithm performance, especially in dense clutter. A novel track initialization based on random sample consensus, named density-based random sample consensus algorithm, is proposed. It utilizes the fact that sequential measurements originating from the same target are contiguous while clutter is separated in space–time domain. The algorithm defines the density property of measurements to decrease the randomness in sampling procedure and increase the efficiency of track initialization. The experimental results show that the density-based random sample consensus is more superior to random sample consensus, the Hough transform algorithm, and logic-based algorithm.