基于多帧杂波稀疏度估计的无源协同定位
Passive coherent location with multi-scan clutter sparsity estimation

针对杂波密度未知时的多目标无源协同定位问题, 提出一种基于多帧杂波稀疏度估计(multi-scan clutter sparsity estimation, MCSE)和高斯混合概率假设密度(Gaussian mixture probability hypothesis density, GMPHD)的多目标无源协同定位算法. 首先, 构建高斯混合后验强度和杂波密度估计之间的反馈模型, 利用门限技术在线剔除潜在的目标测量, 以减少目标测量对杂波密度估计的干扰. 其次, 基于多帧杂波稀疏度估计, 实现非均匀分布的杂波密度的在线估计, 进一步提高杂波密度未知时的多目标跟踪性能. 仿真验证了所提算法的有效性.
In order to solve the problem of multi-target passive coherent location in clutter with unknown density, a multi-scan clutter sparsity estimation and Gaussian mixture probability hypothesis density (MCSE-GMPHD) based multi-target passive coherent location algorithm is proposed. First, a feedback model that connecting the Gaussian mixture posteriori intensity with the clutter density estimation is constructed. The potential target-originated measurements are eliminated by a designed threshold, which helps to reduce the effect on the clutter density estimation of the target-originated measurements. Second, a multi-scan clutter sparsity estimator is proposed to estimate the nonuniform clutter density online, that can improve the tracking performance with unknown clutter density. Simulation results verify the effectiveness of the proposed algorithm.