双未知输入自校准滤波方法  

A Dual-unknown-input Self-calibration Filtering Method

作　　者：傅惠民[1] 杨海峰 FU Huimin;YANG Haifeng(Research Center of Small Sample Technology, Beihang University, Beijing 100083, China)

机构地区：[1]北京航空航天大学小样本技术研究中心

出　　处：《控制与信息技术》2019年第4期1-5,共5页CONTROL AND INFORMATION TECHNOLOGY

基　　金：国家重点基础研究发展计划(2012CB720000);工信部2018年智能制造综合标准化项目《基于数字仿真的机械产品可靠性测试方法标准研究与试验验证》

摘　　要：在通信、导航、制导与控制、故障诊断等许多工程领域中,由于环境因素的影响、模型和参数的不当选取、量测设备故障等原因,系统状态方程和量测方程中往往含有未知输入(未知系统误差),而传统的Kalman滤波方法却无法消除这两种未知输入的影响,导致产生较大的滤波误差。为此,文章提出一种双未知输入自校准滤波方法,分别对线性系统和非线性系统进行了详细讨论,并给出了相应的公式和计算步骤。该方法能够自动识别状态方程和量测方程中有无未知输入,当有未知输入时,能对其进行自动估计、补偿和修正。大量实例计算和仿真模拟结果表明,与传统方法相比,该方法能够有效提高状态估计精度,且计算简单,便于工程应用。In many engineering fields, such as communication, navigation, guidance and control, fault diagnosis and so on, state equations and measurement equations often contain unknown inputs(systematic errors) due to the influence of environmental factors, improper selection of models and parameters, and failure of measuring equipment. Traditional Kalman filtering methods cannot eliminate the effect of these two unknown inputs, leading to large filtering errors. For this reason, a dual-unknown-input self-calibration filtering method was proposed. The linear and non-linear systems were discussed in detail, and the corresponding formulas and calculation steps were given. This method can automatically identify the unknown inputs in state equations and measurement equations, and automatically estimate, compensate and modify them subsequently. Large number of examples and simulations show that, as compared to traditional methods, the proposed method can effectively improve the accuracy of the state estimation. In addition, the calculation is simple, which is convenient for engineering applications.

关 键 词：滤波 双未知输入 系统误差 自校准 导航 故障诊断 状态方程 量测方程 

分 类 号：V448[航空宇航科学与技术—飞行器设计]