Robust Adaptive Cubature Kalman Filter and Its Application in Relative Navigation

doi:10.3969/j.issn.1000-1093.2018.01.010

Abstract

Abstract: An adaptive Huber-based cubature Kalman filter (CKF) algorithm with noise estimator is proposed to solve the problem that the measurement noise of vision-based relative navigation sensor for unmanned aerial vehicles (UVAs) formation follows non-Gaussian distribution. The Huber technique based on solving the linear regression problem and the covariance matching method are combined in the proposed algorithm. The residual sequences are used to estimate and tune the statistical characteristics of process noise and measurement noise on line, and then the received measured data are weighted by using the forgetting weighted parameters, thus estimating the relative position, relative velocity and relative attitude information among unmanned aerial vehicles accurately, and improving the adaptive capability of Huber-based CKF algorithm. The simulated results show that the proposed algorithm has strong adaptability to the statistical properties of the contaminated noises, higher estimation accuracy and stronger robustness compared with the standard CKF algorithm. Key

Key words: unmannedaerialvehicle, relativenavigation, non-Gaussiannoise, robustadaptivefiltering, robustness

CLC Number:

V249.32⁺1

ZHANG Xu, CUI Nai-gang, WANG Xiao-gang, CUI Hu-tao, QIN Wu-tao. Robust Adaptive Cubature Kalman Filter and Its Application in Relative Navigation[J]. Acta Armamentarii, 2018, 39(1): 94-100.

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第39卷
第1期2018 年1月兵工学报ACTA
ARMAMENTARIIVol.39No.1Jan.2018

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