孙小君^{1, 2},
周晗^1,,,
沈海滨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)，黑龙江省高校基本科研业务费黑龙江大学专项资金(2020-KYYWF-0998)

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，1993年生，硕士生，研究方向为多传感器信息融合、系统辨识
沈海滨：男，1994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:周晗　1120546259@qq.com

中图分类号:TN713, TP18

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被引次数:0

出版历程

收稿日期:2020-02-21
修回日期:2021-03-07
网络出版日期:2021-03-29
刊出日期:2021-12-21

Weighted Fusion Robust Incremental Kalman Filter

Xiaojun SUN^{1, 2},
Han ZHOU^1,,,
Haibin SHEN¹,
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 Special Funds of Heilongjiang University of Basic Scientific Research Expenses for Colleges and Universities in Heilongjiang Province (2020-KYYWF-0998)


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。同样地，当系统的噪声方差不确定时，滤波的性能也将会变坏，甚至会引起滤波器发散。增量方程的引入可以有效消除系统的未知量测误差，从而带未知量测误差的欠观测系统的状态估计问题可以转换为增量系统的状态估计问题。该文考虑带未知量测误差和未知噪声方差的线性离散系统，首先提出一种基于增量方程的鲁棒增量Kalman滤波器。进而，基于线性最小方差最优融合准则，提出一种加权融合鲁棒增量Kalman滤波算法。仿真实例证明了所提算法的有效性和可行性。
关键词:信息融合/
加权融合/
欠观测系统/
增量滤波/
鲁棒性
Abstract:Under certain environmental conditions, when the measurement equation of the system is not verified or calibrated, the use of the measurement equation will often produce unknown system errors, resulting in large filtering errors. Similarly, when the noise variance of the system is uncertain, the performance of the filter will deteriorate, and even cause the filter divergence. The introduction of incremental equation can effectively eliminate the unknown measurement error of the system, so that the state estimation of system under poor observation condition with unknown measurement error can be transformed into the state estimation of incremental system. In this paper, a robust incremental Kalman filter based on incremental equation is proposed for linear discrete systems with unknown measurement error and unknown noise variance. Then, based on the linear minimum variance optimal fusion criterion, a weighted fusion robust incremental Kalman filtering algorithm is proposed. Simulation results show the effectiveness and feasibility of the proposed algorithm.
Key words:Information fusion/
Weighted fusion/
Systems under poor observation condition/
Incremental filtering/
Robustness



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