Research On Technologies Of INS/Geomagnetic Matching Integrated Navigation System

With the fast development of the geomagnetic measurement technology and the related studies, the comprehensive advantages of geomagnetic navigation have become increasingly prominent. The positioning errors of geomagnetic matching navigation do not accumulate over time and have long-term stability, which can compensate the error accumulated over time in inertial navigation system (INS). INS can provide high-precision reference of short-term for geomagnetic matching positioning, which can improve the efficiency and accuracy of matching. INS/geomagnetic matching integrated positioning is becoming a new navigation technology, which can satisfy "long-term, self-contained, high-precision, all-weather" navigation requirements.The large-scale high precision data for navigation are inadequate. Based on the geomagnetic measurement data, Earth Magnetic Anomaly Grid 2 (EMAG2), provided by International Association of Geomagnetism and Aeronomy (IAGA), a new interpolation method is proposed for improving resolution of the geomagnetic map. Kriging method widely used in spatial data interpolation is unbiased optimal. It connects the two adjoining points with a straight line or a higher-order smooth curve that leads to "smooth effect" or "low-pass filter", so it is difficult to get the details of changes and non-average features between two points. Therefore, the fractal geometry theory is introduced to the geomagnetic data interpolation. Here the Multifractal Measure Kriging (MFMK) interpolation method is proposed. MFMK combines Kriging method with multifractal measure. It can not only show the trends of geomagnetic anomaly in geometric domain, but also maintain and enhance the local information of data structure.Geomagnetic anomaly field is a potential field, and there are large areas with similar characteristics. Therefore, selecting the suitable-matching region(SMR) is very important. Geomagnetic anomaly data are nonlinear and self-similarity. By defining feature indexes, the estimation accuracy, scattering, undulations within unit surface, irregular degree of geomagnetic anomaly regions are analyzed for selecting the SMR, and the performance of regional matching is evaluated on this basis. There has not been a universal method for SMR selection now. It mostly depends on the matching probability and matching error of each region to division standard by man-made in the existing references. In this paper, a SMR selection method based on principal component analysis(PCA) was proposed. It can evaluate the suitability of regions quantitatively and makes the selection problem more simple, intuitive, and effective.When geomagnetic anomaly is chosen as the characteristic variable for matching, the changes of geomagnetic anomaly values are small in some local areas, and that will lead to low precision and accuracy. To solve this problem, multifractal dimension spectrum is introduced for the extracting characteristic variable for matching. By describing the local features of the geomagnetic anomaly and the different features of the forming process of geomagnetic anomaly, multifractal dimension spectrum can distinguish the differences between the data from the similar geomagnetic anomaly areas correctly. In most of the published research reports about the geomagnetic matching algorithms, the terrain matching or image processing methods were generally applied for geomagnetic matching navigation. There are no special geomagnetic navigation matching algorithm. In terms of the characteristics of less samples, nonlinearity, high-dimension, high global optimality of geomagnetic anomaly, a geomagnetic matching algorithm based on support vector machine is proposed for pattern classification of latitude and longitude in this paper.. As self-similarity fractal parameter H can reflect the degree of the self-similarity and irregularity of geomagnetic anomaly surface, H was used as the initial value of Gaussian kernel width s. To improve search efficiency and real-time of algorithm, s and penalty factor C were selected together by heuristic search. The steps of the matching algorithm is given. Simulation results show that the algorithm performs effectively.The integrated technology of INS and geomagnetic matching is also studied. The integrated positioning filter is designed. Error of longitude and latitude and other parameters are set as state variables, and the difference of INS output position and geomagnetic location are set as observables so the INS position error can be estimated by filter. The results of integrated positioning simulation on regions of three different characteristics show that INS/geomagnetic matching integrated positioning can inhibit the reducing of INS positioning accuracy for a long time.