A radiofrequency tomography method based on zero-sparse data-driven weight model

A radio frequency tomography, data-driven technology, applied in the field of radio frequency, to achieve the effect of improving efficiency and improving effect

Active Publication Date: 2022-04-15
WUYI UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a radio frequency tomography method based on a zero-sparse data-driven weight model. The method performs radio frequency tomography based on a data-driven weight model, so as to avoid the problems caused by the elliptical weight model and improve radio frequency tomography. The effect of imaging

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  • A radiofrequency tomography method based on zero-sparse data-driven weight model
  • A radiofrequency tomography method based on zero-sparse data-driven weight model
  • A radiofrequency tomography method based on zero-sparse data-driven weight model

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Embodiment 1

[0062] Please refer to figure 1 , the present embodiment provides a radio frequency tomography method based on a zero-sparse data-driven weight model, comprising the following steps:

[0063] S1. Construct a wireless sensor network including a plurality of sensor nodes, and the sensor nodes communicate with each other to form a plurality of links;

[0064] S2. Construct a mathematical weight matrix, the mathematical weight matrix is ​​used to represent the relationship between the shadow loss and the pixel extra loss of each link;

[0065] S3. Construct a training weight model based on the zero-sparse characteristic of the mathematical weight matrix;

[0066] S4. Input the training data into the training weight model for training to obtain the training weight matrix;

[0067] S5. Perform radio frequency tomography based on the training weight matrix.

[0068] Wherein, the training weight matrix includes weight factors of each pixel corresponding to each link.

[0069] In t...

Embodiment 2

[0117] In the second embodiment, Scene 1 and Scene 2 are used as training data to train the training weight model, and the traditional ellipse weight model is used as a comparison example to explore the effect difference between the method of the present invention and the traditional method.

[0118] The ellipse model used for comparison with the present embodiment is as follows:

[0119]

[0120] Among them, φ ij Indicates the shadow fading weight of pixel j with respect to link i, d tx(i),j and d rx(i),j Respectively represent the distance between pixel j and the transmitting node and receiving node of link i, d represents the distance between receiving and transmitting nodes.

[0121] Scene 1 described in this embodiment is an obstacle-free perception area. Such as the real picture of scene 1 ( figure 2 (a1)) and topological graph ( figure 1 As shown in (a2)), there are no obstacles in the scene, but there are walls on the left and right sides of the scene, and des...

Embodiment 3

[0127] In this embodiment, a hands-free positioning experiment is carried out to test the actual positioning effect of the training weight model provided by the present invention.

[0128] In this embodiment, scene 1 and scene 2 are respectively used as training data to input the training weight model, and the training weight matrix based on scene 1 and the training weight matrix based on scene 2 are obtained, and the weight matrices obtained from different scene data are reconstructed respectively. Hands-free target and compare with the localization results of the ellipse weight model. In this embodiment, the Bayesian target estimation algorithm is used for sparse image reconstruction, and the parameter settings in the reconstruction algorithm are the same. In this embodiment, the pixel width is set to 0.2m and 0.4m respectively, which is mainly based on the fact that the width of the human body is generally between 0.2m-0.4m, if the pixel width is set to be greater than 0.4m...

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Abstract

The invention discloses a radio frequency tomography method based on a zero-sparse data-driven weight model, comprising the following steps: constructing a wireless sensor network including a plurality of sensor nodes, wherein the sensor nodes communicate with each other to form a plurality of links; Construct a mathematical weight matrix, which is used to represent the relationship between the shadow loss and pixel extra loss of each link; construct a training weight model based on the zero-sparse characteristic of the mathematical weight matrix; input training data into the training weight The model is trained to obtain a training weight matrix; radio frequency tomography is performed based on the training weight matrix. In the radio frequency tomography weight model design method provided by the present invention, the weight model is not based on the traditional ellipse weight model, can accurately represent the change information of the RSS value in the link caused by the target, and the calculation is not affected by the parameters of the ellipse weight model.

Description

technical field [0001] The invention relates to the radio frequency field, in particular to a radio frequency tomographic imaging method based on a zero-sparse data-driven weight model. Background technique [0002] In the process of radio frequency tomography, the shadow attenuation weight matrix is ​​the link between the RSS value (signal attenuation strength) and the shadow attenuation of the pixel in the perception area, and the setting of the weight factor reflects the signal attenuation of the link to the corresponding pixel attenuation value contribution. The design of the weight matrix needs to reflect the coverage of the measurement link to the pixel and the size of the weight under the coverage. [0003] The current mainstream weight matrix is ​​generally designed based on the elliptic weight model. That is, the receiving and sending nodes are taken as the focus of the ellipse, and L (=d+λ) is taken as the long axis of the ellipse, d is the distance between the r...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B17/391H04W24/06H04W84/18
CPCH04B17/3911H04W24/06H04W84/18
Inventor 郝晓曦郑成勇王国利
Owner WUYI UNIV
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