Wireless local area network (WLAN) indoor positioning method based on overlapping of two sets of fingerprints

Abstract

The invention discloses a wireless local area network (WLAN) indoor positioning method based on overlapping of two sets of fingerprints, and belongs to the technical field of wireless communication. The WLAN indoor positioning method based on overlapping of the two sets of fingerprints is used for positioning the indoor personnel / asset in a WLAN environment. According to the method, signal features of reference points are firstly collected in an off-line period, and a first RSS fingerprint database is built; position coordinates of all the reference points in the first fingerprint database are translated, the two sets of fingerprints intersect and are overlapped, then the signal features of the translated reference points are collected, and a second RSS fingerprint database is built. In an on-line positioning period, grid positions of a point to be positioned in two fingerprint maps are judged respectively, an average value of the grid center point coordinates is obtained, and positioning is carried out; if the local positioning environment is not ideal, the best set of fingerprints is selected through a fingerprint intersecting screening method, and the influence on positioning calculation from several abnormal fingerprints is effectively reduced. The WLAN indoor positioning method based on overlapping of the two sets of fingerprints is simple, easy to do and high in positioning accuracy.

Description

technical field [0001] The invention belongs to the technical field of wireless communication. The invention is a WLAN indoor positioning method based on the superposition of double sets of fingerprints, which is suitable for indoor personnel / asset positioning in a wireless local area network environment. Background technique [0002] With the rise and rapid development of the Internet of Things, location-based services are more and more widely used, and location-based services require positioning systems to provide accurate, real-time, and reliable location information. Therefore, it has important practical significance to make full use of existing wireless local area networks (Wireless Local Area Networks, WLAN) to provide accurate location information in an indoor environment at a lower cost. [0003] At present, the most widely used indoor positioning method is the position fingerprint method, which uses the signal strength RSS (Received Signal Strength) measured by the ...

AI Technical Summary

Problems solved by technology

Theoretically, the positioning accuracy is improved with the increase of the reference point density. Since the offline stage needs manual participation in the establishment of the fingerprint database, in order to obtain higher positioning accuracy, the offline workload will also increase, which will increase the time cost rapidly.

[0005] Patent name: WLAN indoor KNN positioning method based on neighbor point optimization, patent application number: 201010154412.2, year 2010, discloses a WLAN indoor KNN positioning method based on neighbor point optimization, which solves the existing WLAN indoor KNN positioning In the method, the positioning accuracy is deteriorated due to improper selection of the number of neighbor points. However, the method divides the target area into a uniform grid in the offline stage, and needs to manually collect the RSS of each reference point, and the offline workload is very large.

[0006] Patent name: WLAN indoor positioning method based on partition information entropy gain, patent application number: 201210329662.4, year: 2012, a WLAN indoor positioning method based on partition information entropy gain is disclosed. Positioning space partitions to reduce the amount of calculation required for positioning and improve positioning accuracy by screening APs. In order to reduce system errors and increase the density of reference points, however, this method divides the target area into uniform grids with an interval of 1 meter in the offline stage. It is necessary to manually collect the RSS of each reference point, which requires a lot of offline workload

[0007] Patent name: Indoor positioning method based on classification threshold and signal strength weight, patent application number: 201310155441.4, year 2013, discloses an indoor positioning method based on classification threshold and signal strength weight. Point classification is used to determine the matching threshold and the reference point signal strength is used as the weight to participate in K-nearest neighbor weighting, which can reduce the influence of RSS random jitter caused by indoor environmental interference, weaken or even eliminate the influence of seriously interfered reference points, and achieve indoor positioning more accurately However, this method divides the target area into a uniform grid with an interval of 1.5 meters in the offline stage, and needs to manually collect the RSS of each reference point, and the offline workload is very large

Images