In the actual integrated navigation process of the MEMS three-axis magnetometer, excessive measurement noise and external magnetic field interference often cause the magnetometer heading to be inaccurate, thereby reducing the overall performance of the integrated navigation system. In order to overcome the above influence, this paper proposes an adaptive real-time filtering noise reduction and comprehensive error interference compensation method. We have completed the construction of the measurement noise model and parameter update by analyzing the measurement noise characteristics of the MEMS three-axis magnetometer. We designed an adaptive filtering algorithm with multiple fading memory factors to perform offline noise reduction on the output data of the magnetometer, and use the noise-reduced data for comprehensive compensation of magnetic field interference, which has a good effect. The experimental results show that the proposed method has better filtering and noise reduction ability and higher compensation accuracy in offline state, can effectively reduce noise and compensate the magnetometer, and improve the accuracy of the magnetometer’s heading calculation.

In practical applications, an electronic compass composed of a three-axis magnetometer and a three-axis accelerometer is often due to the strong magnetic field interference of the carrier, which makes the magnetometer unable to accurately calculate the heading, and even causes the electronic compass to fail. This problem puts forward more stringent requirements on the installation and use of the electronic compass on the carrier. Generally, traditional magnetic field compensation methods such as ellipse/ellipsoid fitting method can effectively compensate electronic compasses. However, these methods cannot be used when the output of the magnetometer is saturated. In order to overcome the above influence, this paper proposes a three-axis external coil active compensation method. By analyzing the error characteristics of the magnetometer, a coil compensation model was established, and a neural network-based control algorithm was designed to realize the adaptive compensation of current. The use of genetic algorithm can improve the global search ability, while controlling the convergence of the BP neural network, which has better real-time performance and higher reliability. The simulation experiment results show that the method has better optimization ability and higher compensation accuracy, the strong magnetic interference of the carrier is effectively compensated, and the working environment adaptability of the MEMS electronic compass is significantly improved.

Aiming at the problems that the DVL calibration process in SINS/DVL integrated navigation is tedious and the measurement information is contaminated by outliers during the alignment of the moving base, we studied IABC fast calibration algorithm and MSTEKF alignment algorithm respectively. Firstly, we optimize the initial population using chaos strategy to improve the calibration accuracy of IABC and avoid the algorithm falling into local optimum. After the DVL calibration, we combined the M-estimation and STEKF to obtain MSTEKF, which was used to suppress the outlier interference in the alignment of the moving base. The experimental results show that IABC can effectively improve the DVL calibration accuracy, and the running speed of the algorithm is increased by about 41.47% compared with PSO. Compared with KF and SHAKF, MSTEKF can effectively suppress outlier interference, and the position alignment error is reduced by 90.00% and 61.31%, respectively.

The submarine is an underwater ship that can perform a variety of combat missions in a complex marine environment. Path planning of submarines has always been the focus of military marine engineering research. In most practical applications, there are numerous marine physical phenomena in the marine environment, such as pycnocline, density fronts, mesoscale eddy, etc., which have an important impact on the navigation of submarines. First, the artificial potential field heuristic factor is introduced into the ant colony algorithm to improve its convergence speed, and the artificial potential field ant colony optimization (APF-ACO) is obtained. In addition, this article uses the unit-body to reflect the regional physical elements and quantifies the physical marine phenomenon in the form of the cost function, which is used to solve the problem of submarine path planning in the complex marine environment. In this article, the algorithm is tested in a real marine data environment. The experimental results show that the algorithm can realize the utilization of the ocean sound speed environment, ocean density environment and ocean current environment, and obtain a path more suitable for submarine underwater navigation.

Based on the application background of magnetic directional navigation on ground-unmanned platforms. The motion of the ground carrier is limited to the two-dimensional plane, which causes the observation matrix composed of magnetic data to be seriously ill conditioned, and it is difficult for traditional methods to estimate stable compensation parameters. In this paper, a regularization method is introduced and combined with Truncated Total Least Squares (TTLS) method and Tikhonov regularization to solve ill-conditioned problems. We control the stability of the solution by introducing regularization variables. During the test, the carrier did not need to travel a specific trajectory (such as an "8" shape) and did not rely on attitude assistance equipment. We verified this method through ground-mounted tests. Test results show that in the case of strong external magnetic interference, this method can solve the ill-conditioned problem of the magnetometer observation matrix by using relatively simple operations, and effectively improve the compensation accuracy of carrier interference magnetic field. The RMSE of the compensated magnetic heading error is better than 0.9°, which is approximately 75% higher than that of the PSO. This method lays the foundation for magnetic field assisted inertial navigation.