非线性估计理论的最新进展

扩展Kalman滤波(EKF)是应用最广的非线性估计方法，然而它存在实现性差、计算量大、估计精度低等缺陷，这些问题起源于。EKF采用了Taylor展开近似。在阐明非线性估计的本质，剖析EKF等传统方法的特点及缺陷的基础上，从非线性估计革新的两条发展思路——非Taylor展开的线性变换及非线性变换出发，分别对插值滤波、Unscented滤波、粒子滤波和神经网络滤波这四种近年来最具特色的新方法进行介绍和评述。通过分析这些方法的工作原理、性能特点、必要性和可行性，将非线性估计最新进展的思想传承、本质内涵、地位与作用予以展现，指出各方法的现存问题、发展潜力和最具可实现性的发展方向。同时强调了各种算法的选取须根据具体应用场合和条件，在主要性能指标之间综合权衡。

New Developments in Nonlinear Systems Estimation

The Extended Kalman Filter (EKF) has unquestionably been the most widely used estimation algorithm for nonlinear systems. However, the EKF is based on first-order Taylor approximations of state transition and observation equations about the estimated state trajectory. The EKF provides an insufficiently accurate representation in many cases, and it is difficult to implement and tune. Many of these difficulties arise from its use of Taylor linearization. To overcome this limitation, the interpolation filtering, unscented filtering, particle filtering and neural network filtering are developed as new nonlinear estimation methods in these years. From the two developmental ways (non-Taylor linearization linear transformation and nonlinear transformation) of nonlinear estimation innovation, these new approaches' motivation, operational principle and performance is reviewed in details, furthermore, some underlying assumptions, flaws and future challenges are pointed out. Although these new algorithms represent sufficient superiority to be applied in many highly nonlinear filtering and control applications, it is important to recognize that the selection of these algorithms should suit measures to local conditions.