Aiming at the problem that the quality of measurement information directly affects the performance of cooperative localization of multiple autonomous underwater vehicles (multi-AUV), a design method for the optimal formation configuration of multi-AUV is proposed to select the best measurement information. First, for the two cases of whether the measurement information is correlated with the measurement error, the performance evaluation functions of formation configuration of multi-AUV are established based on the information ellipse area. Second, through in-depth analysis of these functions, the design methods of the optimal formation configuration in two cases are proposed respectively. Subsequently, taking the multi-AUV cooperative localization systems with four-master and five-master as examples, based on the proposed design method, the optimal formation configuration for cooperative localization system of multi-AUV in two cases is studied deeply, and the results show that it is dynamic. Besides, a special formation configuration is proposed for the cooperative localization system of multi-AUV, which is easier to be implemented compared with the optimal formation configuration when the localization performance is almost the same. At last, the simulation experiments verify the correctness of the proposed method and the effectiveness of the proposed special formation configuration.

It is of great significance to measure real-time surface vessel magnetic field characteristics based on underwater vehicle equipped with magnetometers. In order to obtain the position information of the vehicle at the bottom of the vessel, it is crucial to estimate its speed and apply it to the auxiliary navigation of acoustic positioning systems. In this paper, we proposed a novel method for estimating moving velocity of an underwater vehicle by processing anomaly magnetic field signals measured by two magnetometers. Firstly, we established a detecting model for anomaly magnetic field of a fixed magnetic object using two magnetometers at different positions on the same straight line. Theoretical analysis based on the detecting model indicated that the speed of underwater vehicle can be obtained by fusing the spatial distance of the magnetometers and the time interval of the anomaly magnetic field waveforms. Then, the sparrow search algorithm was introduced to estimate the time interval considering that this optimization algorithm has the advantage of simple structure and fast convergence. Finally, computer simulation and real-word experiments have been conducted to verify the performance of the method. The results demonstrated that the method can calculate speed of the magnetometers correctly with an error of less than 2% and calculation time less than 3s. The features of easy implementation and low computational complexity make the proposed method a potential candidate for the application in the underwater assisted navigation and other fields.

In order to solve the problem that the cesium optical pump magnetometer is disturbed by the carrier’s interference magnetic field during magnetic field anomaly detection, an interference magnetic field compensation method based on an improved mayfly optimization algorithm (IMOA) was proposed in this paper. First, by combining the measurement results of the attitude sensor with the geomagnetic inclination and magnetic declination in the locality, the measurement results of the optical pump magnetometer can be decomposed into the component values under the three axes of the carrier coordinate system. A compensation model including the carrier interference magnetic field was established. Then, considering the poor global search performance that existed in the mayfly optimization algorithm (MOA), an elite chaotic reverse learning strategy and Levy mutation strategy were introduced to improve the MOA. The compensation performance of the IMOA was estimated with a series of field experiments and compared with the stretching particle swarm optimization algorithm. The experiment results indicated that these two methods can effectively compensate the magnetometer’s measurement values, and that the IMOA method more easily jumps out of the local optimum, and has higher compensation accuracy.

The advancement of unmanned platforms is driving the miniaturization and cost reduction of the multi-beam echosounder (MBES). In the process of MBES array calibration, the mutual coupling significantly impacts the performance of parameter estimation. We propose a correction method to mitigate the mutual coupling effects in the calibration of MBES acoustic array. Initially, a near-field focused beamforming model is established to assess the influence of mutual coupling. Subsequently, the covariance matrix in the frequency domain is constructed to enhance algorithm efficiency and simplify solution procedures. This construction eliminates the need for a low-pass filtering step after heterodyning through extracting peak values near zero frequency in the signal frequency domain. Meanwhile, the Toeplitz property is leveraged to render the estimation results independent of the mutual coupling matrix. Finally, the mutual coupling coefficients and the direction of arrival (DOA) are joint-estimated and the Cramér–Rao bound is derived. The presented method effectively addresses the engineering challenge of MBES mutual coupling calibration. Additionally, the performance of the proposed method is verified through the measured data in simulation and tank experiments.