结合幅度信息的扩展目标随机有限集跟踪方法

The random finite set-based extended target tracking methods generally partition measurements by spatial information. It is possible to place clutter measurements into target cells in a dense clutter environment resulting in degradation of tracking performance. To solve this issue, in this paper, the amplitude information of the target and clutter was introduced into the Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter, and thus, the optimal partition was found by calculating the amplitude likelihood of the measurement cells. Additionally, when calculating the centroid of a measurement cell, amplitude was used as a weighting factor to find the mass center instead of the widely used geometric center. This further reduced clutter interference. The tracking results of Swerling 1 fluctuating targets in a Rayleigh clutter when the signal-to-clutter ratios were 13 dB and 6 dB showed that the performance of the proposed algorithm in cardinality estimation and state estimation was better than that of the GIW-PHD filter.