改进的最适高斯近似概率假设密度滤波

最适高斯近似概率假设密度滤波是一种新颖的多机动目标跟踪算法。然而，该算法存在模型概率先验固化问题，即在计算模型概率的过程中量测信息不起作用。针对以上问题，该文提出一种改进算法，通过引入模型概率更新过程，将后验量测信息加入模型概率的计算式中，根据似然函数在多个运动模型之间进行软切换，进而实现对多个机动目标的有效跟踪。实验结果表明，改进算法能够有效解决模型概率先验固化问题，在目标数估计和滤波精度方面均优于传统算法，具有良好的工程应用前景。 

Improved Best-fitting Gaussian Approximation PHD Filter

The best-fitting Gaussian approximation Probability Hypothesis Density (PHD) filter is a novel algorithm for multiple maneuvering target tracking. However, there is a problem that the model probabilities are calculated without the measurement innovation. To solve this problem, an improved algorithm is proposed in this paper, which develops an update procedure for model probabilities to employ the posterior measurement innovation to enhance the filtering performance. Then, the dynamic equations can be softly switched among different models according to the likelihood functions. The simulation results show that the improved algorithm has the advantages over the ordinary one in the aspects of target number estimation and filtering accuracy, implying good application prospect.