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To deal with the influence of abnormal underwater acoustic ranging errors in cooperative localization of autonomous underwater vehicles, a novel measurement information anomaly detection method based on adaptive neuro-fuzzy inference system (ANFIS) is proposed. The method can accurately identify and isolate acoustic distance information with errors exceeding the threshold range even when multiple distance information is alternately used as the measurement data for the filtering algorithm. The adaptive cubature Kalman filter is used to extract the characteristic information, which can better reflect the change of measurement information. According to the preset state threshold, the flag bit obeying Bernoulli distribution is obtained, and the hybrid database is established. This method combines the online data training mechanism with the ANFIS-based detection system to update the ANFIS rules online, which can effectively improve the reliability and accuracy of anomaly detection method, especially when the sample data are insufficient. Experimental results based on data obtained from the actual lake-water trial show that the method can accurately identify the abnormal acoustic distance information and retain the accurate distance information to ensure the stable operation of cooperative localization system.

In this paper, an improved maximum correntropy cubature kalman filter(IMCCKF) is proposed to address the measurement outliers in cooperative localization(CL) of autonomous underwater vehicles (AUVs). The estimated performance of the maximum correntropy cubature kalman filter(MCCKF) algorithm is affected by the kernel bandwidth(KB). The selection value of the KB cannot be determined only by experience in practical CL of AUVs, which will greatly reduce the practical application value of the MCCKF algorithm. The adaptive factor is constructed by comparing the trace size of innovation matrix and the trace size of quantity prediction error matrix, and the KB in the MCCKF is adjusted online by the adaptive factor. Finally, the validity of the proposed IMCCKF method is verified by the lake test data. The experimental results show that the proposed method has the ability to adjust the KB in real time and quickly obtain the optimal value of the KB, and the IMCCKF algorithm can effectively improve the positioning performance of CL system with measurement outliers.

We are concerned with the uniform regularity estimates and vanishing viscosity limit of solution to two dimensional viscous compressible magnetohydrodynamics (MHD) equations with transverse background magnetic field. When the magnetic field is assumed to be transverse to the boundary and the tangential component of magnetic field satisfies zero Neumann boundary condition, even though the velocity is imposed the no-slip boundary condition, the uniform regularity estimates of solution and its derivatives still can be achieved in suitable conormal Sobolev spaces in the half plane $\mathbb{R}^2_+$, and then the vanishing viscosity limit is justified in $L^\infty$ sense based on these uniform regularity estimates and some compactness arguments. At the same time, together with \cite{CLX21}, our results show that the transverse background magnetic field can prevent the strong boundary layer from occurring for compressible magnetohydrodynamics whether there is magnetic diffusion or not., Comment: 33 pages. arXiv admin note: text overlap with arXiv:2108.12969

In this paper, a new cooperative localization (CL) method for multiple autonomous underwater vehicles (AUVs) is proposed to address the problem of measurement outliers and communication packet loss caused by the harsh underwater environment. Combining the advantages of both the maximum correntropy criterion (MCC) and the adaptive neuro-fuzzy inference system (ANFIS), the quality of collected data can be improved by MCC and the ANFIS can be better trained. The efficacy of the proposed method in the CL of AUVs is verified by lake trial. The experimental results show that ANFIS can effectively obtain the location of AUVs based on the input data when the communication packet is lost and the combination of MCC and ANFIS provides better positioning accuracy and robustness. When the probability of measurement outliers is 2%, the proposed method reduces the averaged localization error by 80%, the standard deviation by 84%, and the maximum error difference by 73% compared with the CL method based on cubature Kalman filter(CKF). Finally, the effectiveness of this method is verified by various experiments under different measurement outliers probabilities.

This paper proposes a new quaternion calibration algorithm to calibrate large misalignment angles between Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL) in SINS/DVL integrated navigation system. A new SINS/DVL/GPS integrated navigation system is derived to complete the SINS alignment and SINS/DVL integrated navigation system calibration. Different from the traditional calibration model, the misalignment angles are described by quaternion, according to the physical properties of misalignment angle and scale factor error, the new measurement equation is derived: zero observation vector is used to estimate the misalignment angles, and velocity error scalar is used to estimate the scale factor error. A switching measurement information Kalman filter is designed to switch between different processes. The performance of the proposed quaternion calibration algorithm is evaluated through simulation comparisons with nonlinear model approaches and experiment. The simulation results demonstrate that the proposed quaternion calibration algorithm has better accuracy and robustness than the nonlinear model approaches. The experiment results show that the proposed quaternion calibration algorithm is effective, the SINS/DVL integrated navigation system positioning error after calibration is less than 1.65% mileage in more than 30km travel.