Research On The Construction Method Of Indoor Radio Frequency Location Fingerprint For WSN

In recent years, the increasing development of wireless sensor networks and location-based services are closely linked, providing an effective technical means for positioning services. However, in terms of the complex indoor environment, traditional outdoor positioning technology can't meet the demand of complex indoor environment. Therefore, the study of a kind of applicable to indoor positioning technology has become the hotspot of wireless sensor network positioning technology.In the indoor positioning technology of the current wireless sensor network, the positioning method based on the received signal strength indication with the characteristics of low cost, simple structure and high positioning accuracy has become the mainstream in the field. In this paper, the problem of poor stability, low resolution and high location algorithm complexity in the existing RF fingerprint location method are studied and improved in the following two aspects.(1)The statistical characteristics of frequency signals, transfer characteristics and fluctuation caused by environmental change were in-depth analyzed. On the basis of this, the construction scheme based on unit receive signal strength ratio is given.In order to describe, analyze and compare the characteristics of different location fingerprint and the influence on the accuracy of positioning, in this paper, we give the fingerprint stability analysis based on coefficient of variation analysis method and fingerprint resolution based on Mahalanobis distance calculation method, and the traditional RSSI means fingerprint, the maximum probability value fingerprint and the URSS fingerprint which proposed in this paper has carried on the thorough comparison analysis and the simulation experiment.(2) In order to improve the positioning accuracy, the traditional positioning method is improved, and the positioning and matching method of the European distance weighted correlation coefficient is given. The Euclidean distance and the fingerprint correlation coefficient algorithm are combined to carry out cooperative operation and matching, which can determine the positioning result together, reduce the error generated by the single match, and improve the accuracy of online positioning.The simulation results show that compared with the traditional location fingerprint feature, URSS fingerprint has better stability and higher resolution, can reduce the environmental change of fingerprint caused by the impact, and ultimately improve the accuracy of the positioning system online. Compared with the traditional positioning method, the positioning and matching method based on the Euclidean distance weighted correlation coefficient is used to obtain a better positioning accuracy.