Measurement-driven sequential random sample consensus GM-PHD filter for ballistic target tracking

Abstract In this paper, a new filter named measurement-driven sequential random sample consensus Gaussian mixture probability hypothesis density (MD-S-RANSAC-GM-PHD) filter is proposed for estimating the trajectory of a ballistic target during its coast phase. Unlike the traditional multiple-target tracking (MTT) algorithms that require data association, the proposed method involves modelling the respective collections of targets and measurements as random finite sets (RFS) and applying the PHD recursion to propagate the posterior intensity in time. To generate the new birth target intensity adaptively, a measurement-driven birth intensity estimation algorithm is developed. Since the measurement set used for birth intensity estimation may contain a large amount of clutter, a measurement set pre-processing method based on density-based spatial clustering and sequential random sample consensus (S-RANSAC) algorithm is proposed to eliminate the interference of clutter on generating new target birth intensity. Specifically, the proposed filter extends the standard GM-PHD filter by distinguishing between the persistent and the newborn target, and the extended Kalman filter (EKF) implementation of our proposed filter for ballistic target tracking is also derived. Simulation results illustrate the advantages of our proposed filter in tracking ballistic missile.

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