Abstract
In recent years, with the continuous development of the economic situation, the price of low-end smart phones continues to reduce, the coverage of wireless local area network (WLAN) continues to improve, and individual users pay more and more attention to the real-time information around them, so indoor positioning technology has become a research hotspot. Among them, the indoor positioning based on the location fingerprint method quickly becomes the “Navigator” of indoor positioning direction by virtue of the simplicity of layout, the cost reduction of hardware facilities and the accuracy of positioning effect. However, the traditional indoor positioning methods usually rely on WiFi signal and KNN algorithm. When the KNN algorithm is implemented, there will be a lot of calculation and heavy workload to establish the location fingerprint database offline, and the efficiency and accuracy of online matching positioning points are low. This paper proposes an OKNN algorithm based on the improved KNN algorithm. By improving the efficiency of matching algorithm, the algorithm indirectly improves the positioning accuracy and optimizes the indoor positioning effect.
Highlights
Location based service refers to providing services for mobile users according to their geographic location or known location
The traditional indoor positioning methods usually rely on wireless fidelity (WiFi) signal and KNN algorithm
This paper proposes an OKNN algorithm based on the improved KNN algorithm
Summary
Location based service refers to providing services for mobile users according to their geographic location or known location. The indoor positioning system designed for WLAN is a research hotspot in recent years These indoor positioning systems can be divided into two types: the system of establishing the fingerprint database based on fingerprint and the system of establishing the radio frequency propagation model according to the propagation characteristics. The scheme of using location fingerprint to establish fingerprint database: due to the popularity of radio transmitter, such as WiFi access point and GSM base station, indoor positioning does not need to deploy additional equipment. In the real-time positioning stage, the mobile terminal finds the coordinates closest to the K positions of the fingerprint database by matching the received RSS vector with the established fingerprint database data, and obtains the user location coordinates by taking the geometric average
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