Indoor passive positioning method based on channel state information and support vector machine

Abstract

The invention discloses an indoor passive positioning method based on channel state information and a support vector machine. The method comprises the following steps: firstly preprocessing the acquired channel state information data, performing de-noising and smoothness through the adoption of a density-based spatial clustering of applications with noise and a weight-based moving average algorithm, and then using the principal component analysis algorithm to extract the features. The data after the preprocessing and feature-extracting can reflect the signal change more accurately and the dimension is greatly reduced. The passive positioning adopts two-stage positioning. In the training stage, the large positioning space is divided into sub-regions, the support vector machine classification and regression model is established for each sub-region so as to acquire a statistic model for accurately representing the nonlinear relationship between the position and the signal. The two-stage positioning firstly determines the sub-regions through the classification of the support vector machine, and the precision position is determined in the sub-region through the regression of the support vector machine. The method disclosed by the invention has the beneficial effects that the passive positioning can be performed in the absence of the active participation of the target, and the indoor positioning precision is improved to sub-meter level.

Description

technical field [0001] The invention relates to the field of indoor positioning, in particular to an indoor high-precision passive positioning method based on channel state information. Background technique [0002] Wi-Fi-based wireless local area network has been widely deployed indoors, and it can also provide positioning services while providing data transmission services. Wi-Fi-based indoor positioning solutions do not need to build special hardware facilities, make full use of existing wireless networks, and expand the application range of low-cost positioning to buildings and indoors. At present, most Wi-Fi indoor positioning technologies adopt the active positioning method, that is, to locate the electronic device carried by the target, scan the surrounding Wi-Fi wireless access points (Access Point, AP, usually a wireless router) and their signal strength, and based on this Make a location estimate. However, in many cases, the positioning target does not carry elec...

AI Technical Summary

Problems solved by technology

Since the indoor area is often large and the environment is complex, the unified dependency relationship used in the entire positioning area cannot accurately express the relationship between position coordinates and signal fingerprints, and thus cannot accurately locate

In order to ensure the positioning accuracy, a large amount of fingerprint data will be collected in a large positioning area, resulting in an increase in computational complexity

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