Mixing chamber indoor location method by using inert navigation and Wi-Fi fingerprint

Abstract

The invention discloses a mixing chamber indoor location method by using inert navigation and Wi-Fi fingerprint. The mixing chamber indoor location method by using the inert navigation and the Wi-Fi fingerprint comprises the following steps: collecting the MAC address of a scanned access point (AP) of a point to be located and a corresponding RSS value, combining the collected MAC address and corresponding RSS value with a Wi-Fi fingerprint library, and performing Wi-Fi fingerprint location on the point to be located; computing a Wi-Fi fingerprint location result and a corresponding time inert navigation location result to obtain the final located position of the point to be located; updating the Wi-Fi fingerprint library by using the inert navigation during location. The mixing chamber indoor location method by using the inert navigation and the Wi-Fi fingerprint has the advantages that the advantages that the inert navigation and the Wi-Fi location are combined, the inert navigation result is corrected by using the Wi-Fi fingerprint location result, the problems of an inaccurate result of the Wi-Fi fingerprint location at an open space and the large human cost because the fingerprint library should be maintained by professional persons are solved, and a location system can keep high location performance for a long term.

Description

technical field [0001] The invention belongs to the field of wireless indoor positioning, in particular to a hybrid indoor positioning method utilizing inertial navigation and Wi-Fi fingerprints. Background technique [0002] The popularity of smart mobile terminals and mobile computing has drawn more and more attention to the research of wireless indoor positioning. Currently the most popular indoor positioning methods include Wi-Fi location fingerprint positioning and dead reckoning. Wi-Fi location fingerprint positioning is divided into two stages: offline training and online positioning. The main work of the offline training phase is to collect wireless access points (APs) and their corresponding signal strength (RSS) values ​​in the area to be located, store the reference point coordinates and the RSS information of the corresponding APs in the database, and establish a location fingerprint database ; In the online positioning stage, according to a certain matching al...

AI Technical Summary

Problems solved by technology

This method has the following disadvantages: First, there is a certain degree of fluctuation in the Wi-Fi signal in the indoor environment, and the opening and closing of hotspots will also cause changes in Wi-Fi, which means that the fingerprint library established in the offline stage needs to be updated frequently and in a timely manner. Otherwise, the positioning results will be inaccurate. However, if the fingerprint library is regularly maintained and updated by professionals, it will be very labor-intensive. An efficient method is needed to automatically update the fingerprint library; Level positioning, but the positioning of open spaces such as corridor areas is inaccurate. This is because the Wi-Fi signal in a closed room is greatly different from the Wi-Fi information outside the room due to the wall barrier, so it is easy to distinguish the difference between the inside and outside of the wall. location, but since there is no barrier in the open space, the signal attenuation is not obvious, and the Wi-Fi information is not much different, so it is not easy to distinguish between different locations

The disadvantage of this method is that if the user is always in motion, the cumulative error of the positioning result will continue to increase, and this method also does not consider the update and maintenance of the Wi-Fi fingerprint database, and cannot maintain high positioning accuracy for a long time

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