Improved Probability Hypothesis Density Filter for Close Target Tracking in Cluttered Scene

To deal with the problem of unsatisfactory performance of close target tracking in cluttered environmental by using the standard probability hypothesis density (PHD) filter, this paper presented an improved PHD filter. First, the track management scheme was proposed to correct the number of targets and clutters. As a result, the number of required particles was reduced. In the measurement update step, the matrix consisted of both particle weight and clutter intensity was built to reassign weight proportion under the unchanged sum of all elements based on the row-column operation. The numeric simulation indicated that the proposed filter can effectively track occluded target in cluttered environment with stable track estimation and accurate cardinality statistics.