Hybrid aerospace positioning method based on barometer and WiFi

Abstract

The invention discloses a hybrid aerospace positioning method based on a barometer and WiFi. The method comprises the specific steps of S1, collecting data of all users of a whole building in a crowdsourcing mode, wherein the data mainly comprises RSS vectors and baro values of positions at which the users are located; S2, preprocessing data uploaded by all users through a cloud service platform; S3, calibrating the preprocessed data by the cod service platform, thereby obtaining standard data; S4, carrying out data analysis clustering on the calibrated data, thereby obtaining a Baro-RSS fingerprint map of the building; and S5, carrying out real-time positioning on the positions at which the users are located by the users themselves. According to the method, the floor positioning problem is solved through the hierarchical clustering analysis of the baro and the K-Means clustering of the WiFi, and the satisfactory positioning accuracy is achieved in a true environment.

Description

technical field [0001] The invention belongs to the technical field of floor positioning, and specifically relates to a hybrid floor positioning method based on a barometer and WiFi. Background technique [0002] In modern life, positioning can be divided into two areas: indoor positioning and outdoor positioning. The existing outdoor positioning is very mature, and there are outdoor positioning systems, such as the Global Positioning System (Global Position System), the Galileo Satellite Navigation System (Galileo Navigation System) and the Beidou-1 dual-star positioning system independently developed by China. As the most mature technology and the most stable positioning system at present, the GPS system uses satellite timing and ranging for navigation, and can provide high-precision continuous position, direction, Velocity, attitude and timing information. The system is composed of 24 satellites distributed on three orbital planes at 120 degrees to each other, of which ...

AI Technical Summary

Problems solved by technology

[0005] 1) Due to the complexity and barrier of the indoor environment, the GSM signal changes significantly indoors with the movement of the user, which will lead to a decrease in positioning accuracy. It can also be said that GSM is not a fine-grained signal, and its own accuracy is not high.

[0006] 2) In the data collection stage, users are required to participate in the calibration of the floors, and at the same time, each building needs to be calibrated, which greatly increases the user's use cost and affects the user's interactive experience, resulting in poor applicability

[0007] 3) Not suitable for mobile phones without built-in air pressure sensor

[0008] The machine does not have an air pressure sensor, so it limits its application in the real environment

[0009] 4) The air pressure fingerprint database of the floor obtained by this algorithm does not have the offline nature

The scenario is that the air pressure fingerprint database is trained by data collected by three users' mobile phones. At this time, all three users have left, and new users cannot be located when they come in, because the original fingerprints cannot be converted into the air pressure fingerprint of the current floor.

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