Improved position fingerprint indoor positioning method

Abstract

The invention discloses an improved position fingerprint indoor positioning method. The method comprises an offline stage and an online stage. The method is characterized in that the offline stage comprises the steps of establishing a position fingerprint database by use of reference points and APs, and classifying data in the fingerprint database through a K-means clustering algorithm and obtaining the class cluster to which the to-be-tested point belongs; and the online stage comprises the steps of matching the to-be-tested point with the fingerprint of the class cluster by use of a Bayesian probability algorithm, thereby calculating the position coordinate of the to-be-tested point. According to the improved position fingerprint indoor positioning method, the K-means clustering algorithm is combined with the Bayesian probability algorithm; the data in the fingerprint database is classified by use of the K-means clustering algorithm, thereby obtaining the class cluster to which the to-be-tested point belongs; therefore, the target range is greatly reduced; the to-be-tested point is matched with the fingerprint of the class cluster by use of the Bayesian probability algorithm; and therefore, according to the position fingerprint positioning algorithm, the positioning precision is ensured, the complexity of the algorithm is reduced, the efficiency is improved, and the algorithm has practical value.

Description

technical field [0001] The invention relates to the technical field of wireless positioning, and more specifically, relates to an improved indoor positioning method of position fingerprints. Background technique [0002] With the rapid development of wireless communication, wireless positioning technology has attracted more and more people's attention, especially indoor positioning technology. People hope to know where they are at any time in large shopping malls, public places, underground parking lots, and long high-speed tunnels. location and can reach the destination quickly. The existing relatively popular indoor positioning technologies include the following types, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and location fingerprint positioning (LFP). The above positioning technologies have their own advantages and disadvantages. Among them, location fingerprint positioning has the highest accuracy, but due to its high algorithm c...

AI Technical Summary

Problems solved by technology

However, for the nearest neighbor method, its calculation speed is fast, but its accuracy is not high. Its improved algorithm K-nearest neighbor and K-weighted nearest neighbor also have the problem that the k value cannot be determined. Although the Bayesian probability method has high precision, it also has the problem of The complexity of the algorithm is high, causing problems such as too long positioning time

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