A self-adaptive determination method of grid pixel attenuation value for wireless tomography

Abstract

A method for adaptively determining grid pixel attenuation values ​​for wireless tomography, the method sequentially includes the following steps: (1) deploying around the monitoring area n sensor nodes; (2) calibrate the communication link; (3) determine the first formula i communication link No. j The weight of grid pixels w ij : (4) Carry out Tikhonov regularization image reconstruction on equation (4) to get grid pixel attenuation value. The method of the present invention introduces a distance attenuation factor, so that the grid pixel weight in the ellipse area can be adaptively changed with the distance from the LOS path of the communication link, and the correction of the weight is more suitable for the propagation characteristics of the wireless radio frequency signal. Experiments show that compared with the standard ellipse model, the method of the present invention has a higher degree of energy concentration at the target position of the reconstructed image, and a smaller pseudo-position area, so it can achieve higher positioning accuracy.

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...

AI Technical Summary

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, which is suitable for scenes with a large ellipse coverage area when the communication link distance is short. 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

Images