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Wireless fidelity (Wi-Fi) indoor positioning method

An indoor positioning and algorithm technology, applied in electrical components, wireless communication, etc., can solve the problems of poor real-time performance, huge database, high computational complexity, etc., and achieve good real-time performance, high computational complexity, and high classification accuracy

Inactive Publication Date: 2013-05-08
HARBIN INST OF TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The present invention proposes a Wi-Fi indoor positioning method in order to solve the problems in the traditional Wi-Fi indoor positioning method that the feature information location fingerprint database is too large, the calculation complexity is high in the matching process of the online positioning stage, and the real-time performance is poor.

Method used

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specific Embodiment approach 1

[0022] Specific implementation mode one, combination figure 1 with figure 2 Specifically describe this embodiment, the Wi-Fi indoor positioning method described in this embodiment,

[0023] The Wi-Fi indoor positioning method is based on the indoor Wi-Fi wireless network system. The indoor Wi-Fi wireless network system to be positioned includes Q wireless connection points and mO reference points, which are shared by the entire indoor to be positioned. For q sub-regions, the Wi-Fi location method of the indoor location to be measured is:

[0024] A. When the point to be measured receives the wireless signal strength value sent by the wireless connection point, the support vector machine classifier is used to locate the point to be measured to the corresponding i-th sub-area, and the location fingerprint of the sub-area is obtained And feature transformation matrix A i ; The point to be measured is the object to be positioned,

[0025] Among them, i=1,2,...,q,

[0026] B. Using the f...

specific Embodiment approach 2

[0029] Specific implementation mode two, combination image 3 To describe this embodiment in detail, the difference between this embodiment and the Wi-Fi indoor positioning method described in the first embodiment is that the support vector machine classifier described in step A, the location fingerprint Sub-RadioMap i And feature transformation matrix A i Obtained by the following steps:

[0030] This embodiment is implemented in the offline stage to construct a location fingerprint map and perform partition processing.

[0031] Step 1. Arrange Q wireless connection points in the entire indoor area to be located, so that wireless signals cover the area, and complete Wi-Fi network construction; and record the corresponding coordinates of O reference points, measure and record O reference points in turn Receive wireless signal strength values ​​sent by Q wireless connection points, and use the wireless signal strength values ​​as location feature information to construct an overall l...

specific Embodiment approach 3

[0038] Specific embodiment three. The difference between this embodiment and the Wi-Fi indoor positioning method described in specific embodiment two is that the support vector machine algorithm described in step three is used to train q sub-regions in sequence to obtain the information of any two sub-regions. Boundary matrix between location fingerprints W i,j And the specific process of generating a support vector machine classifier is:

[0039] Arrange Q wireless connection points throughout the indoor area to be located, Input data for the support vector machine algorithm Represents each data to be classified, each reference point receives the wireless signal strength value sent by Q wireless connection points, y t Corresponds to x t The label value of, all x belonging to the i area t The label value y t Is 1, all x belonging to zone j t The label value y t -1, all y before training t The value has been determined, and the i and j are sub-areas of the indoor area to be loca...

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Abstract

The invention particularly relates to a wireless fidelity (Wi-Fi) indoor positioning method, and aims at resolving the problems that in a traditional Wi-Fi indoor positioning method, a feature information position fingerprint map data base is too large, computation complexity in an on-line positioning phase matching process is high, instantaneity is poor, and the like. The method includes that when a point to be detected receives a wireless signal strength value sent by a wireless connection point, a support vector machine classifier is adopted to position the point to be detected to a corresponding ith subregion, and a position fingerprint map and a feature transformational matrix Ai of the subregion are obtained; and the feature transformational matrix Ai of the ith subregion is adopted to enable the wireless signal strength value of the point to be detected to achieve shiftdim, a d-dimension wireless signal strength value is obtained and matched with the subregion, the weight K-nearest neighbor node algorithm is adopted to forecast position coordinates of the point to be detected, and positioning results are output. The Wi-Fi indoor positioning method is applied to the communication field.

Description

Technical field [0001] The invention relates to an indoor positioning method, in particular to a Wi-Fi indoor positioning method. Background technique [0002] With the improvement of the level of technology and the rapid development of multiple multimedia services, people's demand for location services is increasing. Especially in a complex indoor environment, it is often necessary to determine the indoor location information of a mobile terminal or its holder. However, due to the existence of interference factors such as multipath signals and small-scale fading, the positioning accuracy based on indoor transmission models often becomes difficult to meet the requirements. [0003] Although indoor positioning methods such as ultrasonic positioning technology, radio frequency identification technology, ultra-wideband technology, and optical tracking positioning technology can achieve high positioning accuracy, they require a large number of sensors and additional hardware equipment ...

Claims

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Application Information

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IPC IPC(8): H04W64/00
Inventor 张中兆莫云马琳栾斌徐玉滨崔扬
Owner HARBIN INST OF TECH
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