The Optimal State Estimation and IMU Fault Diagnosis of Aircraft Based on Optimal Two-Stage CKF

Abstract

As a typical nonlinear system, the nonlinear state estimation problem of aircraft needs to be solved by nonlinear Kalman filter with high numerical accuracy. The standard Cubature Kalman Filter based on the spherical - radial cubature rule, which could be applied to solve high-dimensional nonlinear filter problems with minimal computational effort. However, when the nonlinear systematic dimension increases, the accuracy of CKF will decline. And CKF will lose the optimality of state estimation with the occurrence of fault. Two-Stage Kalman Filter can solve this problem, but it only applies to linear systems. In order to achieve high-precision estimation of aircraft state and fault diagnosis of the inertial measurement unit (IMU), an optimal two-stage volume Kalman filter (OTSCKF) is proposed in this paper. We take the IMU fault as random deviation and establish the aircraft and filter model. By comparing the simulation results of CKF and OTSCKF on aircraft state estimation and IMU fault diagnosis under the condition of unsteady wind field, we found that OTSCKF can obtain the optimal estimation of system state and deviation under the condition of random deviation of nonlinear system. Compared with CKF, the accuracy of OTSCKF is greatly improved. Moreover, it has the capability of fault diagnosis and robustness to IMU fault.