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Summarizes the technical principles of geomagnetic matching navigation, expounds the navigation solution process of MAGCOM, SITAN and ICCP three main matching navigation algorithms, and discusses the application status of geomagnetic navigation technology from the three geographical environments of satellite, air and underwater. It points out the research prospects of geomagnetic matching navigation and provides reference for the research and development of geomagnetic navigation technology.

This paper presents a real-time calibration algorithm for tri-axial magnetometer. A non-linear state space model for calibration related parameters is derived by the invariance norm of local geomagnetic field, and cubature Kalman filter (CKF) is used to estimate calibrating parameters in sequential manner, which is more suitable for real-time critical applications. The effectiveness of the algorithm are verified by numerical simulations. The results are compared with the traditional TWO-STEP algorithm, and the corresponding conclusions are obtained.

Under the background of Gaussian colored environmental magnetic noise, for the problem that a supervised learning algorithm has high requirements on samples and poor practicability, a magnetic anomaly detection method based on feature fusion and isolation forest (IForest) algorithm is proposed. The method uses different feature algorithms to extract the statistical features, time-frequency features and fractal features of the signal, reduces the dimensionality of the features by principal component analysis (PCA) and generates feature fusion tensors. Finally the IForest algorithm is used to achieve target detection. The simulation and experimental results show that the method has a higher detection rate under different SNR of Gaussian color noise, which is approximately 5%-18% higher than that of the traditional feature detection algorithm. This method can train an effective detection model with only a small number of negative samples. Compared with the fully connected neural network (FCN) model trained with unbalanced samples, the detection rate increases by approximately 5%-12%, and it takes less time.