首页|Adaptive nonlinear Kalman filters based on credibility theory with noise correlation

Adaptive nonlinear Kalman filters based on credibility theory with noise correlation

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To solve the divergence problem and overcome the difficulty in guaranteeing filtering accuracy during estimation of the process noise covariance or the measurement noise covariance with traditional new information-based nonlinear filtering methods,we design a new method for estimating noise statistical characteristics of nonlinear systems based on the credibility Kalman Fil-ter(KF)theory considering noise correlation.This method first extends credibility to the Unscented Kalman Filter(UKF)and Extended Kalman Filter(EKF)based on the credibility theory.Further,an optimization model for nonlinear credibility under noise related conditions is established consid-ering noise correlation.A combination of filtering smoothing and credibility iteration formula is used to improve the real-time performance of the nonlinear adaptive credibility KF algorithm,fur-ther expanding its application scenarios,and the derivation process of the formula theory is pro-vided.Finally,the performance of the nonlinear credibility filtering algorithm is simulated and analyzed from multiple perspectives,and a comparative analysis conducted on specific experimental data.The simulation and experimental results show that the proposed credibility EKF and credi-bility UKF algorithms can estimate the noise covariance more accurately and effectively with lower average estimation time than traditional methods,indicating that the proposed algorithm has stable estimation performance and good real-time performance.

Kalman filterExtended Kalman Filter(EKF)Unscented Kalman Filter(UKF)CredibilityNoise correlation

Quanbo GE、Zihao SONG、Bingtao ZHU、Bingjun ZHANG

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School of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,China

Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology,Nanjing 210044,China

School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China

School of Logistics Engineering,Shanghai Maritime University,Shanghai 201306,China

School of Electronic and Information Engineering,Tongji University,Shanghai 200092,China

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National Natural Science Foundation of ChinaQing Lan Project of Jiangsu Province,ChinaAeronautical Science Foundation of China

62033010R2023Q072019460T5001

2024

10.1016/j.cja.2024.04.016

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(6)
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