Method for evaluating WLAN (Wireless Local Area network) indoor single-source gauss location fingerprint locating performance based on conditional information entropy

A conditional information and fingerprint positioning technology, which is applied in the field of information systems, can solve problems such as high computing and storage overhead, and difficult performance comparison between systems, and achieve the effect of reducing computational complexity and time storage overhead

Inactive Publication Date: 2011-10-12
HARBIN INST OF TECH
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the existing WLAN indoor single-source Gaussian position fingerprint positioning system is difficult to carry out the system due to the particularity of the indoor environment, AP and reference point layout based on the positioning accuracy and exp

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  • Method for evaluating WLAN (Wireless Local Area network) indoor single-source gauss location fingerprint locating performance based on conditional information entropy
  • Method for evaluating WLAN (Wireless Local Area network) indoor single-source gauss location fingerprint locating performance based on conditional information entropy
  • Method for evaluating WLAN (Wireless Local Area network) indoor single-source gauss location fingerprint locating performance based on conditional information entropy

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specific Embodiment approach 1

[0019] Specific implementation mode one: the following combination figure 1 and figure 2 Describe this embodiment, the WLAN indoor single-source Gaussian position fingerprint positioning performance evaluation method based on conditional information entropy described in this embodiment, the method includes the following steps:

[0020] Step 1. Establish the initial reference point distribution model N′×M′:

[0021] For any single-source logarithmic Gaussian distribution positioning environment, with the access point AP as the center and the radius d m Multiple reference points are set on the N′ rings of , and the number of reference points on the mth ring is M′ i , that is, the initial reference point distribution model can be expressed as an N′×M′ low-dimensional asymmetric model, where m=1,…,N′;

[0022] Step 2. Reconstruct the initial reference point distribution model N′×M′ low-dimensional asymmetric model into an N×M high-dimensional symmetric model, in which, the mea...

specific Embodiment approach 2

[0028] Specific implementation mode 2: This implementation mode further explains the implementation mode 1. In step 2, the specific implementation process of reconstructing the initial reference point distribution model N′×M′ low-dimensional asymmetric model into an N×M high-dimensional symmetric model is as follows:

[0029] Step 21: Between the i-th and i+1-th rings of the N′×M′ low-dimensional model, add k n rings, and satisfy ΔW=W i，j -W i+1，j =W s，j -W s+1，j , s=1,...,N-1,

[0030] Among them, W i，j Indicates the reference point R i，j The mean value of the signal strength from the access point AP collected at ;

[0031] Step 22: Add M-M' on the i-th ring i reference points, and the M reference points formed after the increase are required to be evenly distributed on the ring, that is, the argument angle between adjacent reference points on the same ring is 2π / M, and the newly added M-M' i reference point and original M′ i The reference points have the same mean sig...

specific Embodiment approach 3

[0040] Specific implementation mode three: this implementation mode further explains implementation mode two, and the newly added k is obtained in step 23 n The process of the average signal strength of a reference point on a ring is:

[0041] The newly added k between the i-th, i+1 and two rings n The rings have similar signal intensity logarithmic attenuation characteristics, that is, the logarithmic attenuation model W i，j =W 0 -[Pl i，j (d 0 )+10α i，j log 10 d i +h(f)], the reference distance d 0 = Free space loss Pl at 1m i，j (d 0 ) and path decay exponent α i，j are constants, where W 0 Represents the transmit power of the access point AP, h(f) represents the attenuation factor related to the transmit frequency of the access point AP, then,

[0042] D. 2，2 P 2，1 =F 2，1 ,

[0043] Among them, D 2，2 represents the reference point location matrix, and D 2,2 = 1 ...

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Abstract

The invention relates to a method for evaluating WLAN (Wireless Local Area network) indoor single-source gauss location fingerprint locating performance based on conditional information entropy and belongs to the field of information system. The invention aims to solve the problems that the traditional medical sensing platform has larger volume, high price, not strong function expansibility and higher energy consumption of a wireless transmission protocol. The method comprises the following steps of: 1, establishing an initial reference point distribution model N'*M'; 2, refactoring the initial reference point distribution model N'*M' from a low dimensional asymmetric model to an N*M high dimensional symmetric model; 3, calculating to obtain a mathematical dependency relationship among the conditional information entropy, location fingerprint locating accuracy and an anticipation error according to the N*M high dimensional symmetric model refactored in the step 2; and 4, evaluating performance influences on the entity location fingerprint positioning system from signal variances of different test points or density changes of reference points according to the mathematical dependency relationship obtained in step 3 by utilizing change situations of the conditional information entropy.

Description

technical field [0001] The invention relates to a WLAN indoor single-source Gaussian position fingerprint positioning performance evaluation method based on conditional information entropy, belonging to the field of information systems. Background technique [0002] Since GPS (Global Positioning System, Global Positioning System) and cellular wireless positioning system have serious shadow shading and multipath effects in indoor environments, the positioning accuracy (usually above 10m) often cannot meet the requirements of location-based positioning in actual indoor environments. application service needs. In addition, the traditional TOA (Time of Arrive, based on time of arrival), TDOA (Time Difference of Arrive, time difference of arrival) and AOA (Angle of Arrive, angle of arrival) positioning methods all put forward higher softness for positioning base stations or terminals. , hardware requirements, therefore, it is not suitable as the core technology of future ubiquit...

Claims

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Application Information

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IPC IPC(8): H04W16/22H04W64/00H04W84/12
Inventor 马琳周牧徐玉滨孟维晓李利民
Owner HARBIN INST OF TECH
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