Optimization Technology Research On Location Fingerprint Indoor Positioning Based On Fuzzy Clustering

With the rapid development of wireless communication and smart mobile devices technologies, there is increasing demanding on location-aware services. High-precision indoor positioning technology is the core technology for location-aware services. With the wide distribution and utilization of indoor WLAN, WLAN-based positioning system has become a hot research topic. The indoor positioning technology based on location fingerprint WLAN has many advantages, such as simple realization, inexpensive, achieve positioning without knowing the location of AP and transmit power, less demand for additional hardware. Location fingerprinting positioning technology is divided into two stages:offline sampling stage and online positioning stage, the current location fingerprint technology in achieving positioning, both in accuracy and efficiency of location fingerprinting positioning algorithm is still no perfect mechanism, therefore, this paper mainly to improve and optimize this problem of location fingerprinting positioning.Compared with serval typical location fingerprinting positioning algorithm, KNN positioning algorithm has certain advantages on time complexity and accuracy, but KNN positioning algorithm is time-consuming when locating. Since K is fixed and this decreases the positioning accuracy for some position, so it’s necessary to improve the existing KNN algorithm base on other algorithm.In order to improve the shortcomings of KNN localization algorithm, this research presents a fuzzy clustering algorithm,To locate link large fingerprint database using clustering analysis method to realize fuzzy clustering, then KNN algorithm was adopted to realize mobile terminal positioning.Fingerprint positioning technology to the position of the improved performance by using MATLAB simulation test, and on the Android platform through a prototype system for experimental verification.Respectively by simulation experiments on the traditional KNN algorithm and the improved KNN algorithm carries on the analysis comparison, in does not affect the other performance of the positioning system mechanism, the final position of the experimental results show that the improved fingerprint orientation on time performance and the matching efficiency are greatly improved.