Indoor positioning method based on WLAN

Abstract

The invention discloses an indoor positioning method based on WLAN, and belongs to the field of indoor wireless communication and network technologies. The method includes the steps that RSSI data, collected by sampling points, of all APs are preprocessed, and one-dimensional vectors and two-dimensional vectors are extracted from the RSSI data and respectively serve as feature vectors; clustering analysis is carried out on the feature vectors, and an area to be positioned is divided into multiple positioning subareas; classification models corresponding to groups of the feature vectors are respectively trained in terms of the feature vectors in each group; the subarea set with the largest number of votes is selected from all the subareas on the basis of the classification models and in combination with a voting mechanism; the method of two rounds of positioning is adopted to narrow the set range of the subareas, and positioning accuracy is improved. According to the method, spatial distribution characteristics of RSSI are fully mined and utilized, and the problems of wide-range indoor positioning, too large searching and matching space and high computational complexity are solved; a novel positioning model is established, and the problems that in an existing WLAN indoor positioning method, the nonlinear and non-Gaussian statistical properties of RSSI signals, caused by non-line-of-sight transmission effect, RSSI attenuation law abnormity and the like, cannot be learned and adapted to are solved.

Description

technical field [0001] The invention relates to a positioning method in the field of WLAN indoor positioning, and belongs to the technical field of indoor wireless communication and network. Background technique [0002] In recent years, with the continuous improvement of people's material living standards, people's demand for location-based services is also increasing day by day, such as in personnel dispatching, asset management, emergency rescue, safety monitoring, safety dispatching, intelligent transportation, map navigation, travel guides, etc. There is a wide demand for positioning; especially in emergency situations, such as emergency rescue, disaster relief emergency command and dispatch and other special application scenarios, positioning information is even more important. [0003] With the in-depth research of ubiquitous computer and distributed communication technology, the rapid development of indoor wireless communication and network technology, derived from W...

AI Technical Summary

Problems solved by technology

The fingerprint-based indoor positioning method, in practical applications, has the disadvantages of large space matching search range, high computational complexity, and large storage space requirements for large-scale indoor positioning. In practical applications, the probability distribution of the RSSI signal at a fixed reference point presents non-Gaussian, non-linear, and multimodal characteristics, which makes the fitted probability distribution function quite different from the actual probability distribution, resulting in time-consuming positioning problems. large matching error

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