Method for optimizing WLAN (Wireless Local Area Network) indoor ANN (Artificial Neural Network) positioning based on FCM (fuzzy C-mean) and least-squares curve surface fitting methods

A technology of least squares and localization method, applied in biological neural network models, network planning, electrical components, etc., can solve the problems of the generalization ability of ANN system decline and the deterioration of localization error.

Inactive Publication Date: 2010-07-14
HARBIN INST OF TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the generalization ability of the ANN system and the deterioration of t

Method used

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  • Method for optimizing WLAN (Wireless Local Area Network) indoor ANN (Artificial Neural Network) positioning based on FCM (fuzzy C-mean) and least-squares curve surface fitting methods
  • Method for optimizing WLAN (Wireless Local Area Network) indoor ANN (Artificial Neural Network) positioning based on FCM (fuzzy C-mean) and least-squares curve surface fitting methods
  • Method for optimizing WLAN (Wireless Local Area Network) indoor ANN (Artificial Neural Network) positioning based on FCM (fuzzy C-mean) and least-squares curve surface fitting methods

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specific Embodiment approach 1

[0019] Indoor optimized ANN positioning method for WLAN based on FCM and least squares surface fitting method. It includes the following steps:

[0020] 1. Given the location of the access point AP and the reference point in the target positioning area, ensure that the distance between adjacent reference points is 1m, and the signal strength from at least one AP can be collected at any reference point, and the signal power strength is greater than -100dBm;

[0021] 2. Establish a two-dimensional Cartesian coordinate system for the target positioning area, save the spatial coordinate values ​​of all reference points and their corresponding reference points, signal strength samples and sample averages from different APs, and establish a positioning fingerprint database;

[0022] 3. Determine the number of clusters C, and use the FCM method to cluster the mean values ​​of signal strength samples at different reference points into C categories, and obtain C cluster centers;

[0...

specific Embodiment approach 2

[0029] Specific implementation mode two: WLAN indoor optimized ANN positioning method based on FCM and least square surface fitting method. It includes the following steps:

[0030] 1. The access point AP (Access Point) and the reference point position in the target positioning area are given, and the distance between adjacent reference points is guaranteed to be 1m, such as figure 1 shown. In addition, the signal strength from at least one AP can be collected at any reference point, and the received SNR (Signal to Noise Ratio) is greater than 5dB.

[0031] 2. Establish a two-dimensional Cartesian coordinate system for the target positioning area, save the spatial coordinate values ​​of all reference points and the signal strength samples and sample average values ​​collected at the corresponding reference points from different APs, and establish a positioning fingerprint database. Its fingerprint data structure is as follows figure 2 shown.

[0032] 3. Determine the numb...

specific Embodiment approach 3

[0062] Specific implementation mode three: an example is given below for analysis:

[0063] The selected experimental scenario and the location of the AP are as follows: Figure 4 shown. In addition, due to the large area of ​​the experimental scene, the choice of Figure 4 Room 1211 in is used as a positioning scene to verify the effectiveness of the present invention, and its outline, reference points and test point positions are as follows figure 1 shown.

[0064] The positioning area is regular and the coverage performance is good, and the WLAN signal sample values ​​from AP1, AP2, AP3, AP8 and AP9 can be detected at any position in the area. Using the NetStumbler signal acquisition software, at each reference point, collect WLAN signals for 3 minutes, sampling twice per second; at each test point, collect WLAN signals for 1 minute, sampling twice per second. Due to the large amount of data, only the original WLAN collected signal samples at the reference point (x=1, y...

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Abstract

The invention discloses a method for optimizing WLAN (Wireless Local Area Network) indoor ANN (Artificial Neural Network) positioning based on FCM (fuzzy C-means) and least-squares curve surface fitting methods, relating to an indoor positioning method used for indoor positioning and aiming to solve generalization capability reduction of an ANN system caused by singular reference points existing in a training sample space. The method comprises the following steps of carrying out clustering on pre-labeled reference points based on the FCM method to confirm corresponding clustering centers and membership degree of different reference points to clustering centers thereof; obtaining the space position of the singular reference points in a target positioning area on the basis of carrying out quantitative processing and similarity calculation on the membership degree of the reference points; updating positioning fingerprint database by utilizing the least-squares curve surface fitting method to reject abrupt change points in an intensity distribution chart; estimating the cluster of a terminal on the basis of calculating the Euclidean distance between signal intensity samples collected online and different clustering centers; and finally accurately estimating the terminal by utilizing corresponding ANN subsystems.

Description

technical field [0001] The invention relates to an artificial intelligence indoor positioning method in the field of pattern recognition, in particular to a WLAN indoor positioning method. Background technique [0002] In recent years, with the rapid development of short-range radio technology and the advancement of wireless local area network technology, indoor positioning technology has advanced by leaps and bounds. In an open outdoor environment, the Global Positioning System (GPS) provides very accurate positioning information, but in an indoor environment, the GPS system cannot be used because the satellite signal is blocked. People not only need location information in an open environment, but also have an increasing demand for positioning information in indoor environments. In indoor environments such as airports, exhibition halls, office buildings, warehouses, underground parking lots, prisons, and military training bases, positioning information is also required. T...

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Application Information

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IPC IPC(8): H04W16/20H04W64/00H04W84/12G06N3/06
Inventor 徐玉滨周牧马琳沙学军孟维晓谭学治
Owner HARBIN INST OF TECH
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