Design And Research Of Indoor Location Algorithm Based On RSSI And Geomagnetic Field Feature Fusion

With the development of network technology and portable intelligent terminal hardware technology, mature sensor technology and network technology are used widely in many fields. In indoor localization technology, a new application field, researchers use sensor network technology and intelligent terminal as auxiliary so that they make the accurate spatial localization realized. High precision localization has a very broad application prospects, such as commercial shopping guide, advertising information precision push, and even in some special industries, such as disaster relief and rescue and so on. Localization technology, especially the indoor localization technology is the key to the final localization service realization.At present, the most of indoor localization realization use the math model which is based on the wireless signal localization technology to estimate the final result. The wireless localization technology fully depends on the characteristics of wireless signal in the localization area. Because of the vulnerability of wireless signal during the transmission process and the fact that the mathematics model of the wireless signal based localization algorithm is an estimation model, there are always some errors that will interfere with the accuracy. To improve the accuracy, much more hardware and facilities are needed. At the same time, the investment is too high to realize the localization algorithm in this way. With the development of microelectromechanical systems (MEMS) sensor, the popularization of intelligent terminals, sensors such as gravity, gyroscope, geomagnetic has been widely used. These sensors can measure the movement data and the environmental features so that we can use them to assist the localization. In this paper, considering the characteristics of the wireless signal localization algorithm, we use the ambient information and the movement data together. This paper presents a fusion of the geomagnetic localization algorithm and indoor integrated localization method of wireless signal localization algorithm based on motion state, which calculate people’s location by the geomagnetic field and wireless signal characteristics. With the convenience of wireless signal technology and magnetic field characteristics, the algorithm will become more accurate, more stable, more efficient and more economical.In this paper, we first introduce the background of the project and the development of wireless signal localization algorithm and geomagnetic localization technology. Then we introduce the principle of the RSSI algorithm and the main techniques and methods used to RSSI localization algorithm. The math model of the RSSI is also mentioned and analyzed in the paper. The experiment and analysis of the advanced RSSI localization algorithm based on motion state are also made. Secondly, the article presents the basic principle of geomagnetic matching technology, implementation and application of technology. Then advantages and disadvantages of various geomagnetic matching technology are analyzed. Thirdly, we also introduced the geomagnetic map reconstruction. In order to improve the localization accuracy of the algorithm, we mainly study the spatial interpolation. According to the algorithm characteristics, we prefer to use the Natural Neighbor Interpolation Method to insert the precision rational valuation to reconstruct the geomagnetic map. And then we do a detailed analysis of the principle. Fourthly, the principle and methods of the Hausdorff measurement are described. At last, the localization fusion algorithm based on RSSI and magnetic field characteristics is illustrated in the paper.In the implementation process of the whole algorithm, we improve the algorithm by two-step optimization. In matching step, we use different methods to calculate the final localization, step by step, from coarsely to precisely. And improve the accuracy of localization estimation localization with the guarantee of both the efficiency and stability of the algorithm. In the end of paper, we made a simulation and experiment. The measured results show that the algorithm has better localization accuracy. And it proves that the method has certain feasibility.