Vehicle Integrated Navigation Based on Velocity Constraint and Fuzzy Adaptive Filtering

doi:10.3969/j.issn.1000-1093.2020.02.003

Abstract

Abstract: The satellite signal is easily obstructed to reduce the positioning precision of the integrated navigation system for the vehicle integrated navigation system. An integrated navigation scheme is proposed, which is assisted by the vehicle velocity constraints. The Kalman filter measurement equation is deduced by taking the side and up velocities of normal running vehicle be zero. A new adaptive Kalman filtering (ADKF) algorithm is deduced in consideration that it is difficult to determine the statistical characteristics of integrated navigation measurement noise. After calculating the ratio of actual covariance to theoretical covariance of innovation sequence, the fuzzy inference system (FIS) is used to adjust the Kalman's measurement noise covariance matrix adaptively. The verification experiments were carried out by using fiber optic strapdown inertial navigation system (SINS). The experimental results show that, when the satellite signal is invalid, the integration of virtual velocities can improve the positioning accuracy of SINS, and the maximum latitude error is reduced from 41.33 m to 8.61 m. The positioning accuracies of the vehicle's three directions calculated by the proposed FIS-ADKF integrated navigation algorithm are more than 60 percent higher than those calculated by the standard Kalman filtering algorithm, which verifies the effectiveness of the proposed algorithm. Key

Key words: integratednavigation, velocityconstraint, adaptivefiltering, fuzzyinferencesystem, strapdowninertialnavigationsystem

CLC Number:

U463.67⁺5

HU Jie， YAN Yongjie， WANG Zihui. Vehicle Integrated Navigation Based on Velocity Constraint and Fuzzy Adaptive Filtering[J]. Acta Armamentarii, 2020, 41(2): 231-238.

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第41卷
第2期2020 年2月兵工学报ACTA
ARMAMENTARIIVol.41No.2Feb.2020

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