Indoor Positioning Method Based on CSI Space-Frequency Characteristics and Reference Point Position Clustering Algorithm

Abstract

The invention belongs to the field of indoor positioning technology, and discloses an indoor positioning method based on CSI space-frequency characteristics and a reference point location clustering algorithm. According to the method, CSI data of which a physical layer is finer-grained and more robust is taken as physical information to ensure that the perceptual ability of the system is much higher than that of RSS, and the influence of multi-path effects can be effectively avoided; CSI space-frequency characteristic vector clustering and reference point location multi-index clustering technologies are adopted, members of which the geographical locations are relatively far away from other cluster members in an original cluster are separately divided into a cluster, and thus the phenomenon that reference points which have relatively far geographical locations and approximate CSI space-frequency characteristic vectors are merged into the same cluster can be avoided; and a biclustering technology including CSI space-frequency characteristic clustering and reference point location clustering is adopted, and the clusters that should not be split are merged into a cluster again, the integrity of the original clusters can be ensured while the processing of singular points is completed, and thus a better clustering effect can be achieved.

Description

technical field [0001] The invention belongs to the technical field of indoor positioning, and in particular relates to an indoor positioning method based on CSI space-frequency characteristics and a reference point position clustering algorithm. Background technique [0002] At present, with the development of wireless networks and the widespread deployment of wireless local area networks, Wi-Fi-based indoor positioning technology has received extensive attention. In the indoor environment covered by Wi-Fi network, by measuring the physical information from the access point APs, using the principle of data fingerprint matching for positioning. This positioning algorithm based on location fingerprints is widely used because of its high positioning accuracy, full use of existing facilities, and little impact on users for upgrades and maintenance. In order to improve the positioning accuracy and efficiency, it is necessary to preprocess the physical information and fingerprin...

AI Technical Summary

Problems solved by technology

[0005] To sum up, the problems existing in the existing technology are as follows: First, physical information mostly uses RSS, which represents the superposition effect of multipath, cannot display channel characteristics, is easily interfered by multipath effect, and is not stable enough to fluctuate over time. Considering the abundant indoor multipath interference and temporal dynamics, RSSI values ​​cannot provide sufficient recognition and robustness in complex indoor environments

The second is that currently multiple data classes use a single index clustering method, but due to the influence of external factors such as access point layout, building structure, and people's walking, the collection of fingerprint data will be disturbed, and it may be classified as Improper clusters directly lead to a decrease in positioning accuracy

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