High-performance indoor positioning method and system based on random forest

Abstract

The invention belongs to the technical field of wireless communication, and discloses a high-performance indoor positioning method based on a random forest. According to the indoor positioning method,in the process of establishing a position fingerprint database, firstly, access points with poor signal strength and unstable signals are deleted according to the signal strength of the access points; and an access point with a better positioning effect is selected from the remaining access points by using an information gain method to form an access point set. On this basis, a location fingerprint database representing location characteristics is established; and then, position clustering is carried out on all positions in the positioning scene through a clustering algorithm, and then a high-precision and high-stability random forest model is constructed for each position cluster. The position of the user is determined by using a random forest model in the positioning process. By constructing the high-precision and high-stability random forest model, the problems that a single decision tree model is limited in positioning precision, unstable in positioning effect and prone to fallinginto overfitting can be effectively solved, and the positioning stability and positioning precision are improved.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a random forest-based high-performance indoor positioning method and an indoor positioning system. Background technique [0002] With the rapid development of society, in the highly urbanized modern society, people's demand for spatial location information continues to increase, and positioning technology has attracted more and more attention. Especially in recent years, location information services based on GPS positioning system, mobile Internet, smart phones and other technologies have brought great convenience to people's daily life. People can use the location services it provides to hail taxis, order food, find banks and even make friends and many other applications. [0003] In the current application scenarios of positioning technology, in addition to the mature outdoor positioning scenarios, indoor positioning scenarios are gradually attracti...

AI Technical Summary

Problems solved by technology

However, in the actual working environment, sometimes the average number of reference positions in the group is still large after grouping, and it is often impossible to reduce the number of layers of the decision tree through grouping

However, if the decision tree has too many layers or too many branches, it is easy to overfit during the decision process, which will affect the effect of classification and positioning.

Although the over-fitting problem can be alleviated to a certain extent by pruning technology in the algorithm process, pruning will increase the ambiguity of the decision at the same time, thereby reducing the positioning accuracy, and will also lead to unstable classification results and affect positioning. algorithm stability

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