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Semi-supervised learning indoor positioning method based on support vector machine

A support vector machine and semi-supervised learning technology, applied in the field of indoor positioning based on support vector machine semi-supervised learning, can solve problems such as large positioning error, affecting positioning accuracy, and large amount of data, achieving high utilization efficiency and wide application scenarios. , the effect of high real-time positioning accuracy

Active Publication Date: 2014-04-02
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0002] In today's indoor positioning technology, there are two commonly used methods for positioning based on WiFi signal strength: one is to use the empirical model of signal loss to obtain the intersection of multiple positioning circles or the intersection of multiple positioning lines as a position estimate by solving the positioning circle equation , because the error of the empirical formula varies greatly with different scenarios, the positioning error of this method is very large; the second is to establish the required empirical database through a large number of training data sampling and data processing in the indoor environment in the early stage, and then use the machine The learning method determines the position. This method has high positioning accuracy, but the early sampling data process is time-consuming and laborious, and the collected data sample set not only has a large amount of data, but also cannot be updated in real time due to the time-varying characteristics of the wireless channel. Error, affecting the positioning accuracy

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  • Semi-supervised learning indoor positioning method based on support vector machine
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  • Semi-supervised learning indoor positioning method based on support vector machine

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[0036] The present invention will be described in detail below in conjunction with the accompanying drawings. This method is researched based on the method of support vector machine in machine learning, which ensures the universality of the algorithm. Using the combination of theoretical analysis, feasibility demonstration and computer simulation, the proposed scheme is verified from the aspects of theory and practice. Including the following content.

[0037] 1. Data collection stage of wireless signal strength distribution:

[0038] In the present invention, the environment requiring indoor positioning needs to be divided into square grids with a certain length and width in advance, and each grid corresponds to a corresponding number, that is, a location label. In each grid, the received signal strength (RSS) data from the WiFi wireless access point (AP, Access Point) in the coverage area is collected. The collection process can include the following two methods:

[0039]...

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Abstract

The invention relates to a semi-supervised learning indoor positioning method based on a support vector machine. The positioning method comprises the following steps: S1) carrying out mesh generation to the indoor positioning environment, and receiving the received signal strength (RSS) data of a WiFi (wireless fidelity) access point (AP) from a coverage area to each grid; S2) processing the collected data; and S3) positioning a mobile terminal. The semi-supervised learning indoor positioning method based on the support vector machine has the advantages of high real-time positioning precision, high use ratio of collected data and wide applicable scene.

Description

technical field [0001] The invention relates to a radio positioning technology applied to an indoor environment, in particular to a semi-supervised learning indoor positioning method based on a support vector machine. Background technique [0002] In today's indoor positioning technology, there are two commonly used methods for positioning based on WiFi signal strength: one is to use the empirical model of signal loss to obtain the intersection of multiple positioning circles or the intersection of multiple positioning lines as a position estimate by solving the positioning circle equation , because the error of the empirical formula varies greatly with different scenarios, the positioning error of this method is very large; the second is to establish the required empirical database through a large number of training data sampling and data processing in the indoor environment in the early stage, and then use the machine The learning method determines the position. This metho...

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Application Information

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IPC IPC(8): H04W64/00H04W84/12
Inventor 赵军辉曾龙基杨涛杜家娇
Owner BEIJING JIAOTONG UNIV
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