Volume 38, Number 4, August 2020
|Page(s)||806 - 813|
|Published online||06 October 2020|
Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF
School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
2 Shaanxi Provincial Key Laboratory of Flight Control and Simulation Technology, Xi'an 710129, China
In the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a typical nonlinear system. In this paper, the faults of the Inertial Measurement Unit (IMU) are treated as a random bias, and the random walk model is used to describe the fault. The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation. For the IMU fault diagnosis method based on OTSUKF, the covariance of the random walk model is assigned with a constant matrix, and the value of the matrix is initialized empirically. It is very difficult to select a matching matrix in practical applications. For this problem, in this paper, the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique, and an adaptive Two-Stage Unscented Kalman Filter (ATSUKF) is proposed to solve the fault diagnosis problem of the IMU. The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF, and verifies the effectiveness of the proposed adaptive method.
Key words: adaptive Kalman filter / two stage Kalman filter / unscented Kalman filter / inertial measurement unit / fault diagnosis / random walk model / simulation experiment
关键字 : 自适应卡尔曼滤波 / 二步卡尔曼滤波 / 无迹卡尔曼滤波 / 惯性测量单元 / 故障诊断 / 随机游走模型 / 仿真实验
© 2020 Journal of Northwestern Polytechnical University. All rights reserved.
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