摘要
在一定环境条件下,当系统的量测方程没有进行验证或校准时,使用该量测方程往往会产生未知的系统误差,从而导致较大的滤波误差。增量方程的引入可以有效解决欠观测系统的状态估计问题。该文考虑带未知噪声统计的线性离散增量系统,首先提出一种基于新息的噪声统计估计算法。可以得到系统噪声统计的无偏估计。进而,提出一种新的增量系统自适应Kalman滤波算法。相比已有的自适应增量滤波算法,该文所提算法得到的状态估计精度更高。两个仿真实例证明了其有效性和可行性。
Under certain environmental conditions,the unknown system errors often occur and yield to larger filtering errors when the unverified or uncalibrated measurement equation is used.Incremental equation can be introduced,which can effectively solve the problem of state estimation for the systems under poor observation condition.In this paper,the linear discrete incremental system with unknown noise statistics is considered.Firstly,a noise statistics estimation algorithm is proposed based on innovation.The unbiased estimation of system noise statistics can be obtained.Furthermore,a new incremental system adaptive Kalman filtering algorithm is proposed.Compared with the existing adaptive incremental filtering algorithm,the state estimation accuracy of the proposed algorithm is higher.Two simulation examples prove its effectiveness and feasibility.
作者
孙小君
周晗
闫广明
SUN Xiaojun;ZHOU Han;YAN Guangming(Electrical Engineering Institute,Heilongjiang University,Harbin 150080,China;Key Laboratory of Information Fusion Estimation and Detection,Heilongjiang Province,Harbin 150080,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2020年第9期2223-2230,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61104209)
黑龙江大学杰出青年科学基金(JCL201103)
黑龙江大学电子工程重点实验室基金(DZZD2010-5)
黑龙江大学青年科学基金(QL201212)。
关键词
自适应Kalman滤波
增量滤波器
欠观测系统
增量系统
滤波精度
Adaptive Kalman filtering
Incremental filters
Systems under poor observation condition
Incremental systems
Filtering accuracy
