基于高斯混合带势概率假设密度滤波器的未知杂波下多机动目标跟踪算法

多模型的随机有限集(RFS)类方法是一类有效的多机动目标跟踪算法，但是现有算法都假定杂波统计特性先验已知，不适用于未知杂波背景。该文以高斯混合带势概率假设密度滤波器(GMCPHDF)为基础，提出一种未知杂波下的多机动目标跟踪算法。该算法对目标和杂波分别独立建模，通过最优高斯(BFG)估计方法对真实目标的强度函数进行预测，从而使多目标强度函数独立于机动目标的运动模型，实现各时刻真实目标的强度函数、杂波源期望个数以及真实目标和杂波源的混合势分布的迭代。仿真结果表明，该算法能够有效地联合估计多机动目标状态以及杂波期望个数。

Gaussian Mixture Cardinalized Probability Hypothesis Density Filter for Multiple Maneuvering Target Tracking under Unknown Clutter Situation

Considering the limitation of the well-known multiple model formulation of the Random Finite Set (RFS) that the statistics characteristic of clutter is assumed to be known a priori, this paper proposes a new multiple maneuvering target tracking algorithm based on Gaussian Mixture Cardinalized Probability Hypothesis Density Filter (GMCPHDF) in the case of unknown clutter. The proposed method predicts the intensity function of actual target states by Best-Fitting Gaussian (BFG) approximation, which is independent of the target motion model. Then the closed-loop iteration procedure among the intensity function of actual target states, the mean number of clutter generators, and the hybrid cardinality distribution of actual targets and clutter generators is established. The simulation results show that the proposed method can effectively estimate the target number, target states and the mean number of clutters simultaneously.