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K nearest fuzzy clustering WLAN indoor locating method based on REE-P

A fuzzy clustering and indoor positioning technology, applied in positioning, measuring devices, instruments, etc., can solve the problems of reference point positioning error, poor adaptability, global signal adjustment or correction, etc., and achieve the effect of improving positioning accuracy.

Inactive Publication Date: 2010-02-03
HARBIN INST OF TECH
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  • Summary
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide an RSS-P-based K-nearest neighbor fuzzy clustering WLAN indoor positioning method to solve the poor environmental adaptability of the K-nearest neighbor method and the inability to comprehensively consider the global signal distribution information to adjust the selected reference point set Or correction, so some reference points will cause large positioning errors

Method used

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  • K nearest fuzzy clustering WLAN indoor locating method based on REE-P
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  • K nearest fuzzy clustering WLAN indoor locating method based on REE-P

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specific Embodiment approach 1

[0009] Specific Embodiment 1: This embodiment is realized through the following steps: 1. Measure and record the RSS signal received by the user terminal at the desired positioning point; 2. Use the K nearest neighbor method to determine the K most similar to the signal characteristics of the desired positioning point Reference point; 3. Use the fuzzy clustering algorithm to classify the RSS value of the selected reference point, calculate the square of the difference between the component in each cluster center vector and the RSS value from the corresponding AP, and accumulate these values ​​​​in the class , select the class with the smallest sum; 4. For the reference points determined by the K-nearest neighbor method in step 2, use the fuzzy clustering algorithm again to classify the positions of all reference points, and select the same class as that selected according to the RSS classification A class of reference points with the most reference points; 5. Take the union of ...

specific Embodiment approach 2

[0011] Embodiment 2: The difference between this embodiment and Embodiment 1 is that the fuzzy clustering algorithm adopts the fuzzy c mean (Fuzzy c mean) clustering algorithm, that is, the FCM algorithm. The algorithm is as follows:

[0012] The clustering criterion is to find the best combination pair (U, P), so that when the constraint μ is satisfied ik ∈ M hc , make the objective function J m (U,P) min. The general description of the objective function is:

[0013] J m ( U , P ) = Σ k = 1 n Σ ...

specific Embodiment approach 3

[0024] Specific implementation mode three: this implementation mode is realized through the following steps:

[0025] Offline phase:

[0026] Step 1: WLAN indoor positioning network planning and layout. The location of the access point (AP) must first meet the requirements of WLAN communication and ensure uniform and seamless coverage of WLAN signals. On this basis, try to make each desired positioning point receive signals from more than three access points.

[0027] Step 2. Select reference points and test points. Evenly select the reference points, arrange the reference points in a uniform grid-like distribution, and record the position coordinates corresponding to the reference points.

[0028] Step 3: Measure and record the RSS signal of the access point that can be received at each reference point, and construct a location fingerprint map.

[0029] Online phase:

[0030] Step 1. Measure and record the RSS signal received by the user terminal.

[0031] Step 2: Apply...

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Abstract

The invention provides a K nearest fuzzy clustering WLAN indoor locating method based on REE-P, relating to the indoor locating method in the field of identification. The method comprises the following steps of: 1. measuring and recording a RSS signal received by an user terminal at a point to be located; 2. ensuring K reference points which are most similar to the signal characteristic of the point to be located with a K nearest method; 3. classifying the RSS value of the selected reference points with a fuzzy clustering algorithm, computing the square of the difference between component in each clustering center vector and the RSS value from corresponding AP, accumulating the values in the clustering, and selecting one with the lowest sum; 4. reusing the fuzzy clustering algorithm to classify the positions of all the reference points and select the reference points which have the most same reference points as that selected from step 3; and 5. taking the sum of the reference points from step 3 and step 4, and taking the average coordinate of the reference points to be taken as the position of the point to be located. The method solves the problem of error location caused by the reference points of the K nearest method, and is used for identifying the position.

Description

technical field [0001] The invention relates to an indoor positioning method in the field of complex system identification, in particular to a K-nearest neighbor fuzzy clustering WLAN indoor positioning method based on RSS-P (Received Signal Strength and Position). Background technique [0002] Since the advent of the IEEE 802.11 wireless local area network standard, the wireless communication market has been growing rapidly, and the deployment of WLAN in indoor environments has become more and more extensive. Being able to access the Internet anytime and anywhere provides broad development prospects for positioning in an indoor WLAN environment. The positioning algorithm based on location fingerprints has relatively high positioning accuracy, can make full use of existing facilities, does not need to change the hardware of mobile devices, the system does not need or only adds very little additional equipment, and upgrades and maintenance have little impact on users, etc. H...

Claims

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Application Information

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IPC IPC(8): G01S5/02H04W4/00
Inventor 徐玉滨孙永亮马琳沙学军周牧
Owner HARBIN INST OF TECH
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