孙小君^{1, 2,,},
周晗¹,
闫广明^{1, 2}
1.黑龙江大学电子工程学院 哈尔滨 150080
2.黑龙江省信息融合估计与检测重点实验室 哈尔滨 150080
基金项目:国家自然科学基金(61104209)，黑龙江大学****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)

详细信息

作者简介:孙小君：女，1980年生，副教授，研究方向为多传感器信息融合、状态估计、信号处理
周晗：男，1996年生，硕士生，研究方向为多传感器信息融合、系统辨识
闫广明：男，1979年生，讲师，研究方向为多传感器信息融合、状态估计

通讯作者:孙小君　sxj@hlju.edu.cn

中图分类号:TN713; TP18

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出版历程

收稿日期:2019-07-02
修回日期:2020-03-20
网络出版日期:2020-08-06
刊出日期:2020-09-27

Adaptive Incremental Kalman Filter Based on Innovation

Xiaojun SUN^{1, 2,,},
Han ZHOU¹,
Guangming YAN^{1, 2}
1. Electrical Engineering Institute, Heilongjiang University, Harbin 150080, China
2. Key Laboratory of Information Fusion Estimation and Detection, Heilongjiang Province, Harbin 150080, China
Funds:The National Natural Science Foundation of China (61104209), The Outstanding Youth Science Foundation of Heilongjiang University (JCL201103), The Key Laboratory of Electronics Engineering, College of Heilongjiang Province (DZZD2010-5), The Youth Science Foundation of Heilongjiang University (QL201212)


摘要
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。
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
Abstract: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.
Key words:Adaptive Kalman filtering/
Incremental filters/
Systems under poor observation condition/
Incremental systems/
Filtering accuracy



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