SVM-KNN (Support Vector Machine-K Nearest Neighbor)-based indoor positioning method

A technology for indoor positioning and positioning area, applied in electrical components, wireless communication and other directions, can solve the problems of large variation range and short real-time measurement time, and achieve the effect of improving stability and accuracy
CN104619014AInactive Publication Date: 2015-05-13SUN YAT SEN UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SUN YAT SEN UNIV
Publication Date
2015-05-13
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a SVM-KNN (Support Vector Machine-K Nearest Neighbor)-based indoor positioning method. Indoor positioning is specifically realized by combining the SVM with the KNN; the purpose is to improve the accuracy and the stability of positioning. The implementation process of the method comprises an off-line stage and an on-line stage, wherein the off-line stage comprises the following steps: 1, performing rational grid type classification on a region to be positioned; 2, rationally and uniformly collecting enough sample points in each grid region, and collecting signal intensity values of the sample points and numbers of belonging grids; 3, taking each grid as a category, and realizing classification modeling by using a support vector machine classification algorithm; the on-line stage comprises the following steps: classifying points to be positioned by using the constructed classification model, selecting K regions with the maximum probability obtained by classification, and calculating the final positional coordinates of the points to be positioned according to a KNN principle.
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Description

technical field

[0001] The invention relates to an indoor positioning method based on the combination of a support vector machine and KNN, and belongs to the application field of the Internet of Things. Background technique

[0002] With the rise of Internet of Things technology, location-based services are getting more and more attention. For outdoor positioning, there are already very mature positioning technologies, and the applications are also very successful, such as the global positioning system and Beidou navigation system. Compared with outdoor positioning technology, due to the complexity of the indoor environment, the particularity of indoor wireless signal propagation, such as multipath effects, shadow effects, etc. These special circumstances prevent the mature outdoor positioning technology from being directly applied to indoor positioning. For this reason, scholars have studied many positioning methods to achieve indoor positioning.

[0003] Support vector ...

Claims

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