Grid pixel attenuation value adaptive determining method for wireless tomography

Abstract

The invention discloses a grid pixel attenuation value adaptive determining method for wireless tomography, and the method sequentially comprises the following steps: (1), deploying n sensor nodes around a monitoring region; (2), calibrating a communication link; (3), determining a weight value w<ij> of a j-th grid pixel of an i-th communication link in a formula; (4), carrying out the Tikhonov normalized image reconstruction of an equation (4), and obtaining a grid pixel attenuation value. According to the invention, the method introduces a distance attenuation factor, and enables a grid pixel weight value in an elliptical area to adaptively change with the distance from the grid pixel to a communication link LOS path. The correction of the weight value is more matched with the propagation characteristics of a radio frequency signal. Through experiments, the method, compared with a standard elliptical model, is higher in energy focusing degree at a target position of the reconstructedimage, and is smaller in regional area at a pseudo position, so the method can achieve the higher positioning precision.

Description

technical field [0001] The invention belongs to the technical field of wireless tomography, and in particular relates to a method for self-adaptive determination of grid pixel attenuation values ​​for wireless tomography. Background technique [0002] Wireless tomographic imaging technology (radio tomographic imaging, RTI) was proposed by the SPAN laboratory of the University of Utah in 2009. This technology uses low-power, low-cost wireless sensors. Reconstructing the image of the monitoring area provides a new technical approach for target detection and tracking. In order to improve the positioning accuracy of the RTI system, researchers have improved and perfected its various links. In all studies, the improvement of the weight model is very important, and a reasonable weight model can better fit the propagation of radio frequency signals. RTI's classic weight model was first proposed by Joey Wilson and Neal Patwari in 2010, but the classic ellipse weight model only det...

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Problems solved by technology

In order to reflect this feature, the weight of the pixel in the ellipse is set to the inverse of the area of ​​the smallest ellipse where the pixel takes the wireless sensor transceiver node as the focus. This model improves the performance to a certain extent, but the calculation is relatively large

[0005] In 2015, Tian Xiaoping of Beijing Institute of Technology proposed a constant eccentricity ellipse model. This model is suitable for scenarios where the communication link distance is short and the ellipse covers a large area. Fewer pixels can be used for image reconstruction, which greatly reduces the The amount of calculation is increased, but the selection of pixel weights in the ellipse has not been studied

[0006] In 2016, Qian Lei's team at Wuhan University proposed a geometry-based ellipse weight model, which divides the pixels within the ellipse range into LOS paths and NLOS paths, and increases the weight of the pixels in the LOS part, while reducing the pixels in the NLOS part. , but the weight parameters need to be determined through experiments, which is not conducive to real-time positioning

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