Modified prediction partitioning algorithm for Gaussian inverse Wishart probability hypothesis density filter using measurement labeling method

Abstract

The Gaussian inverse Wishart probability hypothesis density filter is a promising approach for tracking multiple extended targets. However, if targets are closely spaced and performing maneuvers, the performance of a Gaussian inverse Wishart probability hypothesis density filter will decline. The reason for this is that the measurement partitioning approaches fail to provide accurate partitions, which influences the component updating process directly. This paper presents a modified prediction partitioning algorithm for the Gaussian inverse Wishart probability hypothesis density filter in order to solve the partitioning problem of closely spaced targets. The inaccuracy of the target prediction information occurred by target maneuvers leads to the above problem, thus a modified prediction partitioning algorithm will label the components and corresponding measurements to improve the prediction accuracy. Simulation results show that the use of modified prediction partitioning can improve the performance of a Gaussian inverse Wishart probability hypothesis density filter by providing more accurate partition results when targets are closely spaced and performing maneuvers.