This paper is concerned with the state estimation problem for nonlinear/non-Gaussian systems suffered from both time-varying disturbances (TVD) and measurement outliers. Conventional particle filtering (PF) approach can be used to track the non-Gaussian probability density functions, but its sampling efficiency is degraded in the presence TVD. To address this problem, we propose a disturbance observer based PF (DOBPF) method where the knowledge on the dynamics of TVD are fully exploited and real-time disturbance compensation is achieved. Furthermore, to enhance the resilience of our method against outliers, we adopt the skew-$t$ distribution to characterize both skewness and heavy-tailedness of the measurement noise. On this basis, the variational Bayes approach is incorporated into the DOBPF under the marginalization PF framework to infer the noise statistics. Compared with conventional PF approaches, the proposed outlier-resilient DOBPF method exhibits improved resilience against measurement outliers and increased sampling efficiency in the presence of TVD. Simulation and experimental results confirm the effectiveness of the proposed algorithm.

本文关注的是非线性/非高斯系统的状态估计问题，该系统同时受到时变扰动（TVD）和测量离群值的影响。可以使用常规的粒子滤波（PF）方法来跟踪非高斯概率密度函数，但是在存在TVD的情况下，其采样效率会降低。为了解决这个问题，我们提出了一种基于干扰观测器的PF（DOBPF）方法，该方法充分利用了TVD动力学知识，并实现了实时干扰补偿。此外，为了增强我们的方法对异常值的适应能力，我们采用了偏斜-$Ť$分布以表征测量噪声的偏度和重尾性。在此基础上，在边缘化PF框架下将变分贝叶斯方法并入DOBPF中以推断噪声统计数据。与传统的PF方法相比，所提出的离群弹性DOBPF方法在TVD存在的情况下表现出了对测量离群值的增强的回弹力和更高的采样效率。仿真和实验结果证明了该算法的有效性。