This article proposes a Gaussian filtering method to approximate the single-target updates and normalizing constants for multitarget tracking with nonlinear, non-Gaussian measurements, and a state-dependent probability of detection. The Gaussian approximation is based on the posterior linearization technique, which seeks the optimal affine approximation of the nonlinearities in a mean square error sense. The normalizing constant is approximated using sigma-points based on the posterior. The proposed approach is implemented in a Poisson multi-Bernoulli mixture filter and compared against standard methods to approximate single-target posteriors and normalizing constants in two range-bearings tracking scenarios.

中文翻译：

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非线性/非高斯测量多目标跟踪的高斯滤波方法

本文提出了一种高斯滤波方法，用于通过非线性、非高斯测量和状态相关的检测概率来近似单目标更新和多目标跟踪的归一化常数。高斯近似基于后验线性化技术，该技术在均方误差意义上寻求非线性的最佳仿射近似。使用基于后验的 sigma 点来近似归一化常数。所提出的方法在泊松多伯努利混合滤波器中实现，并与标准方法进行比较，以在两个距离方位跟踪场景中近似单目标后验和归一化常数。