Random Error Modeling and Compensation of Geomagnetic Map Data

Abstract

The geomagnetic map is an important factor affecting the performance of geomagnetic matching navigation. The random error of the geomagnetic map seriously affect the accuracy of the matching positioning, which even leads to matching failure. In order to improve the geomagnetic matching performance, a method for modeling and compensating the random error of geomagnetic map data is proposed. Based on the analysis of the random error characteristics of the geomagnetic map data, this method establishes a nonstationary time series model of the data, takes this model as the state equation, takes the real-time data as the measurement, and uses Kalman filter to filter the geomagnetic map data to compensate the random error of geomagnetic map data. The effectiveness of the filtering method is indirectly proved through the navigation and positioning experiments based on the geomagnetic data before and after filtering. The processing results of the actual geomagnetic map data show that the geomagnetic map data filtered by the method in this paper can improve the positioning accuracy by 54.7% when it’s used for navigation.