卡尔曼滤波器
控制理论(社会学)
α-β滤光片
移动视界估计
不变扩展卡尔曼滤波器
快速卡尔曼滤波
自适应滤波器
集合卡尔曼滤波器
扩展卡尔曼滤波器
计算机科学
数学
算法
人工智能
控制(管理)
作者
Liuyang Jiang,Hai Zhang
出处
期刊:Automatica
[Elsevier]
日期:2019-02-01
卷期号:100: 396-402
被引量:13
标识
DOI:10.1016/j.automatica.2018.11.037
摘要
Noise distribution plays an essential role in state estimation using Kalman filter. However, statistical characteristics of the noise are often unknown in most practical applications. A second order mutual difference (SOMD) algorithm has been proposed to generate an estimation of the measurement noise covariance matrix R by calculating the autocorrelation of SOMD of redundant measurements, and thus it can avoid coupling with the state estimation error; however, the algorithm cannot be applied directly for a majority of practical systems due to the requirement of redundant measurements. In this paper, the SOMD algorithm is expanded to the system with single measurement by constructing a pseudo measurement. A non-zero estimation bias detection algorithm is presented to address the inconsistency between the mathematical model and the real. A modified robust adaptive Kalman filter (RAKF) is also developed to tackle this inconsistency and improve filtering accuracy by activating adaptive operation properly. The efficacy of the approach is demonstrated via a target tracking problem. Simulation results indicate that the proposed algorithm can reflect the noise properties accurately and outperform several reference algorithms in precision and robustness.
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