【摘要】 Kalman滤波是状态估计和数据去噪领域的重要基础性方法.现代动态系统的高复杂性和传统Kalman滤波假设的局限性使得传统Kalman滤波理论方法已难以满足日益增长的高精准数据分析需求.以经典Kalman滤波理论特点分析为基础,针对系统类型和噪声刻画方式的不同,总结现有Kalman滤波方法的研究进展;并以估计模型与实际系统失配这一客观事实为出发点,根据不同自适应方式深入分析现有自适应Kalman滤波方法体系;给出Kalman滤波理论面向实际系统的工程可用性分析,并介绍了可观测性、可观测度和工程化智能Kalman滤波理论的若干重要研究进展;最后指出工程化智能Kalman滤波研究的几个重要研究方向.更多还原

【Abstract】 The Kalman filter is an important method in the field of state estimation and data denoising. Because of the high complexity of modern dynamical systems and assumptions limitation of the traditional Kalman filtering theory, the conventional Kalman filtering methods suffer diffculty to meet the increasing demand for high-precision data analysis. Firstly, motivated by the analysis on the properties of the conventional Kalman filter, the research of the existing Kalman filtering method is summarized according to different system types and noise characterization ways. Secondly, the existing adaptive Kalman filtering system is deeply discussed through different adaptive ways based on the fact of the mismatch between model and the real system. Then, the engineering usability analysis of the Kalman filtering is shown for practical systems based on the current research, and some important research development are introduced on observability, observable degree and engineering intelligent Kalman filter theory. Finally, several important research directions are given for smart Kalman filtering.更多还原