THE SMOOTH PARTICLE VARIABLE STRUCTURE FILTER

In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the smooth variable structure filter (SVSF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SVSF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictor corrector format. It has been shown to be very robust to modeling errors and uncertainties. The combined method, referred to as the smooth particle variable structure filter (SPVSF), utilizes the estimates and state error covariance of the SVSF to formulate the proposal distribution which generates the particles used by the PF. The SPVSF method is applied on two computer experiments, namely a nonlinear target tracking scenario and estimation of electrohydrostatic actuator parameters. The results are compared with other popular Kalman-based estimation methods.

本文介绍了一种基于粒子滤波器（PF）和平滑变结构滤波器（SVSF）的新的状态和参数估计方法。PF是一种常用的估计方法，它利用分布式质点来形成概率分布函数（PDF）的近似。SVSF是一种基于滑模概念的相对较新的估计策略，以预测 - 校正形式表述。它已被证明对建模误差和不确定性具有很强的鲁棒性。这种组合方法被称为平滑粒子变结构滤波器（SPVSF），它利用SVSF的估计值和状态误差协方差来构建建议分布，从而生成PF所使用的粒子。SPVSF方法应用于两个计算机实验，即非线性目标跟踪场景和电动静液作动器参数估计。其结果与其他常用的基于卡尔曼的估计方法进行了比较。