Magnetic positioning is a promising technique for vehicles in global navigation satellite system (GNSS)-denied scenarios. In general, a fixed-length magnetic sequence is required to provide an initial positioning result, which means that users need to wait a relatively long distance. To minimize this initialization distance, a map-aided fast initialization method, including magnetic database construction and magnetic positioning, is proposed in this paper. For magnetic database construction, a multisource fused database is established using a precise and effective strategy in which the positions of reference points (RPs) and the diverse information of paths are obtained from the map and the magnetic field is calculated using data collected during driving. For magnetic positioning, we innovatively propose a coarse–fine combination method that improves the positioning accuracy within a short distance. In the coarse map matching stage, by detecting the vehicle’s motion and utilizing the topological relationships between paths, the search range is precisely narrowed. In the fine magnetic localization stage, an improved mean absolute difference (MAD) metric and a derivative metric are combined to form a joint matching criterion to determine the positioning result. The experimental results illustrate the importance of each module in the proposed method, which improves the precision of the database up to 80% and significantly shortens the initialization distance up to 50%.

Magnetic navigation is a promising positioning technique for scenarios where a global navigation satellite system (GNSS) is unavailable, such as for underwater submarines and aircraft in space. For ground scenarios, it faces more challenges, since the magnetic distribution suffers interference from surrounding objects such as buildings, bridges, and vehicles. It is natural to think how feasible it is to apply magnetic matching positioning to vehicles. In this paper, a theoretic distribution model is proposed to analyze the magnetic field around objects such as buildings, bridges, and vehicles. According to the experiments, it is shown that the proposed model matches the experimental data well. In addition, a comprehensive indicator metric is defined in this paper to describe the feasibility of the magnetic matching method based on the statistical characteristics of magnetic maps. The best length of matching window, anti-noise performance, and pre-comparison of positioning accuracy in different regions can be easily derived using the proposed comprehensive indicator metric. Finally, the metric is verified through a drive test using different building densities.

Aeromagnetic compensation plays a vital role in geomagnetic navigation and has received considerable attention throughout last few decades. Classical aeromagnetic compensation methods based on the Tolles–Lawson (T-L) model are mainly aimed at permanent, induced, and eddy-current magnetic interferences of aircraft platform, which ignores other stray magnetic field interference on the platform including the interferences caused by on-board electronic (OBE) systems. In order to cooperate with TL model, magnetometers are usually required to be installed on the extension rod outside the cabin, which is widely applied to geophysical magnetic survey. In order to ensure safety and reduce the cost of platform modification in geomagnetic navigation, it’s necessary to place magnetometers inside the cabin. It also further exacerbates the magnetic interferences and improves the difficulty of magnetic compensation. In this paper, a modified aeromagnetic compensation method is proposed, and the in-cabin OBE interferences are respectively modelled to be proportional to the currents and their temporal variations of different electronic devices. To ensure that modified model adapts to strong OBE interference in the cabin, a cut-off frequency determination method based on curvature calculation and a feature selection method based on correlation calculation are proposed. The cut-off frequency determination method helps to select passband filter which suitable for in-cabin OBE interference. The feature selection method can help to effectively select current and voltage inputs for modified model. In addition, principal component analysis (PCA) is adopted to reduce multicollinearity which is intensified by the extension of OBE interferences in coefficient-estimating. Experiments on public dataset are conducted to illustrate the effectiveness of the proposed method, and the best compensation result achieved 1.71nT of root mean square error (RMSE).