Positioning method based on random forest model, device and storage medium

Abstract

The invention provides a positioning method based on a random forest model. The method comprises the steps that the equipment signal data of multiple sets of wireless equipment reported by a mobile terminal are received; the signal intensity data of each set of wireless equipment are extracted from the equipment signal data to act as the eigenvalue, and the eigenvalue of each set of wireless equipment is combined so as to generate a feature vector; the feature vector is inputted to a pre-determined indoor positioning classifier, and multiple possible position coordinates of the mobile terminaland the probability of each possible position coordinates are calculated; and the position coordinate of the highest probability are selected from the prediction result outputted by the indoor positioning classifier and the position coordinate of the highest probability are transmitted to the mobile terminal. The invention also provides an electronic device and a computer readable storage medium.With application of the positioning method based on the random forest model, the device and the storage medium, the positioning accuracy of the mobile terminal can be enhanced.

Description

technical field [0001] The present invention relates to the technical field of wireless positioning, in particular to a positioning method based on a random forest model, an electronic device and a computer-readable storage medium. Background technique [0002] When positioning a mobile terminal in an indoor environment, wireless signal transmitting devices (such as wireless routers) can be deployed in the indoor environment, and the mobile terminal can be deduced based on the signals of these devices detected by the mobile terminal and the known location information of these devices. s position. [0003] With the movement of the mobile terminal, the server constantly interacts with the mobile terminal to continuously locate the location of the mobile terminal. At present, for the selection of indoor positioning classifiers, the improved support vector machine model or the improved K-nearest neighbor classification algorithm are generally used, but in actual use, their abil...

AI Technical Summary

Problems solved by technology

At present, for the selection of indoor positioning classifiers, the improved support vector machine model or the improved K-nearest neighbor classification algorithm are generally used, but in actual use, their ability to deal with missing features is not ideal, so they cannot be used for mobile terminals. Accurate positioning

[0004] Moreover, in practical applications, the signal detected by the mobile terminal may fluctuate due to reasons such as object occlusion, signal interference, or failure of wireless signal transmitting equipment, resulting in signal deviation, but for the server, it will not automatically go Judging whether the signal deviates, just use the signal data to directly calculate the position and return it to the mobile terminal for display, resulting in unreasonable positioning points also being displayed on the mobile, resulting in low positioning accuracy

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