WLAN (Wireless Local Area Network) indoor KNN (K-Nearest Neighbor) positioning method based on near-neighbor point number optimization

A positioning method and technology of neighboring points, applied in the field of pattern recognition, can solve problems such as improper selection of neighboring points, deterioration of positioning accuracy, etc., and achieve the effect of improving effectiveness and reliability

Inactive Publication Date: 2010-11-10
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] In order to solve the problem of worsening positioning accuracy caused by improper selection of neighbor points in the existing WL

Method used

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  • WLAN (Wireless Local Area Network) indoor KNN (K-Nearest Neighbor) positioning method based on near-neighbor point number optimization
  • WLAN (Wireless Local Area Network) indoor KNN (K-Nearest Neighbor) positioning method based on near-neighbor point number optimization
  • WLAN (Wireless Local Area Network) indoor KNN (K-Nearest Neighbor) positioning method based on near-neighbor point number optimization

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specific Embodiment approach 1

[0017] Specific implementation mode one: according to the instructions attached figure 1 and 5 Specifically illustrate this embodiment, the WLAN indoor KNN positioning method based on the optimization of neighbor points described in this embodiment, its positioning process is:

[0018] Step 1: Arrange multiple access points AP for the indoor environment, ensure that any point in the environment is covered by the signal sent by one or more access point APs, and uniformly set N in the indoor environment RP a reference point;

[0019] Step 2: Select a reference point as the coordinate origin O c Establish a two-dimensional Cartesian coordinate system to obtain N RP The coordinate positions of each reference point in the two-dimensional rectangular coordinate system, and use the signal receiver to collect the signal strength RSS value from each access point AP on each reference point, according to the coordinate position of each reference point and the correlation with each ref...

specific Embodiment approach 2

[0027] Specific implementation mode two: according to the instructions attached Figure 6 This embodiment is specifically described. This embodiment is a further description of the first embodiment. In the first embodiment, in step 4, the KNN positioning method when the number of neighbor points k is 1 is obtained. theoretical expectation error The specific method is:

[0028] Step 411: make the number of neighbor points k=1 in the KNN positioning method;

[0029] Step 412: Make the pre-estimated position of the test point i Positioning error is δ, ensure that P j -P T ≤P T -P j+1 ,in, And there are conditions: Confidence probability is obtained according to the stated conditions

[0030] Prob 1 , δ ( ϵ ) = 1 + r 8 N RP ( ...

specific Embodiment approach 3

[0036] Specific implementation mode three: according to the instructions attached figure 2 , 3 , 4, 7 and 8 specifically describe the present embodiment, and this embodiment is a further description of the specific embodiment one or two. In the specific embodiment one or two, in step four, the KNN positioning method when the number of neighbor points k is 2 is obtained About the theoretical expected error of the test point i in the pre-estimated position

[0037] E 2,2 ( ϵ ) = Prob 2,2 , d j + r 2 ( ϵ ) E 2,2 , d j + r 2 ...

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Abstract

The invention relates to a WLAN indoor KNN positioning method based on near-neighbor point number optimization, which relates to the field of mode identification and solves the problem of reduced positioning precision caused by improper near-neighbor point number selection in the traditional WLAN indoor KNN positioning method. The WLAN indoor KNN positioning method comprises the following steps of: firstly establishing a complete WLAN positioning scene and a position fingerprint database; then, pre-estimating the position of a testing point according to the collected signal intensity at the testing point and the pre-stored position fingerprint data by utilizing a KNN positioning method with the near-neighbor number as 2; then obtaining the theoretical expected error of the testing point at a pre-estimated position by the KNN positioning method when the near-neighbor point numbers are 1 and 2, and selecting the near-neighbor point number corresponding to the KNN positioning method with higher theoretical precision as the optimum near-neighbor point number for estimating the position of the testing point; and finally realizing WLAN indoor KNN positioning by utilizing the KNN positioning method under the optimum near-neighbor point number. The invention is applicable to indoor positioning.

Description

technical field [0001] The invention relates to the field of pattern recognition, in particular to a WLAN indoor KNN positioning method based on optimization of neighbor points. Background technique [0002] According to the "National Medium and Long-Term Science and Technology Development Plan (2006-2020)", "National "Eleventh Five-Year" Science and Technology Development Plan" and "863 Program "Eleventh Five-Year" Development Outline" and other national-level scientific and technological strategic planning documents Deployment, earth observation and navigation technology are listed as key frontier exploration topics. Among them, "high-precision seamless navigation and positioning technology" has become an important sub-topic in this field and has received extensive attention. For future mobile users, they not only need to obtain location information in an open environment, but also have an increasing demand for positioning information in an indoor environment. In indoor ...

Claims

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Application Information

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IPC IPC(8): H04W64/00H04W84/12
Inventor 徐玉滨周牧刘宁庆马琳谭学治
Owner HARBIN INST OF TECH
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