GMPHD Based Multi-Scan Clutter Sparsity Estimation
Yunfei Guo, Jinxing Pan, Dongliang Peng, Zhikun Chen, Han Shentu · 2018
In order to solve the problem of multi-target tracking in clutter with unknown density, a Gaussian mixture probability hypothesis density (GMPHD) based multi-scan clutter sparsity estimation (MCSE) algorithm is proposed. First, the GMPHD filter is used to estimate the cardinality and state of the target with the clutter density in last step. Then all Measurements originated from the targets are eliminated online, which helps to reduce the effects on the clutter density estimation of target-originated measurements. Last, a multi-scan clutter sparsity estimation algorithm is proposed to update the current clutter density. Simulation results verify the effectiveness of the proposed algorithm.