存在异常值时采用鲁棒测量更新的固定结构高斯混合滤波
Fixed-structure Gaussian Mixture Filtering with Robust Measurement Updates under Outliers
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中文总结 AI 辅助
针对存在异常值的离散时间非线性随机系统状态估计，提出固定结构高斯混合滤波的鲁棒测量更新方法，通过学生t分布建模异常值影响并结合变分贝叶斯近似，在三维跟踪场景中验证了其有效性。
中文摘要 AI 辅助
本文研究离散时间非线性随机系统在存在测量异常值情况下的贝叶斯状态估计问题。基于固定结构高斯混合滤波框架，本文提出一种鲁棒测量更新变体，其预测密度结构通过将转移密度离线分解为轴对齐高斯分量确定；该构造使高斯混合结构保持确定性，并可通过所选分解保真度进行调优。受异常值影响的测量分量采用学生t分布建模，各高斯混合分量的对应更新通过变分贝叶斯过程近似。所得到的滤波器在三维跟踪场景中进行评估，该场景采用测距和方位角测量，其中方位角通道受建模为重尾噪声的异常值影响。
英文摘要
Bayesian state estimation for discrete-time nonlinear stochastic systems is considered in the presence of measurement outliers. Building on a fixed-structure Gaussian mixture filtering framework, this paper proposes a robust measurement-update variant in which the predictive density structure is determined by an offline decomposition of the transition density into axis-aligned Gaussian components. This construction maintains the Gaussian mixture structure as deterministic and tunable via the chosen decomposition fidelity. Measurement components affected by outliers are modeled using a Student's-t distribution, and the corresponding update of each Gaussian mixture component is approximated by a variational Bayes procedure. The resulting filter is evaluated in a three-dimensional tracking scenario with range and bearing measurements, where the bearing channel is affected by outliers, modeled as heavy-tailed noise.
