Abstract
To deal with the problems of outliers and nonlinearity in the complex underwater environment, a Huber’s M-estimation-based cubature Kalman filter (CKF) is proposed for an inertial navigation system (INS)/Doppler velocity log (DVL) integrated system. First, a loosely coupled INS/DVL integrated system is designed, and the nonlinear system model is established in the case of big misalignment angle. Then, Huber’s M-estimation is introduced for robust estimation to resist outliers. Meanwhile, the CKF is focused to handle the nonlinearity of the state equation. Finally, simulation and the vehicle test are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the conventional Kalman filter (KF) and outlier detection-based robust cubature Kalman filter (RCKF) in terms of navigation accuracy in the complex underwater environment.
Highlights
Increasing attention has been given to underwater navigation for ocean exploration
To solve the nonlinear problem caused by the inertial navigation system (INS) error equations in the case of the big misalignment angle, the cubature Kalman filter (CKF) is studied in the section. It regards the estimation of the nonlinear equation as the estimation of the probability distribution based on the Bayes estimation, which extremely simplifies the nonlinear problem [25]. e basic steps of the CKF are as follows
A weighted matrix is employed to limit the influences of outliers to the system and make full use of the measurement information
Summary
Increasing attention has been given to underwater navigation for ocean exploration. The error equation of the INS is nonlinear in the case of a big misalignment angle, which significantly complicates the estimation problem. It is generally handled by the nonlinear filter [9]. In [14], Huber’s M-estimation-based process uncertainty robust filter was studied in the INS/GPS integrated system. In [15], Huber’s M-estimation was introduced for the variational Bayesian- (VB-) based unscented Kalman filter (UKF) in the integrated navigation system. Huber’s M-estimation-based cubature Kalman filter (MCKF) is proposed for the INS/DVL integrated system in this paper. State Equation. e state equation of the INS/DVL integrated system is established by the error model of the INS. e INS error equations of attitude, velocity, and position can be derived as follows [14]:
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