Sparsity-enabled radio tomographic imaging using quantized received signal strength observations

Mishra, Abhijit; Sahoo, Upendra Kumar; Maiti, Subrata

doi:10.1016/j.dsp.2022.103576

Cited by 7 publications

(1 citation statement)

References 40 publications

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“…This observation, together with the idea that a pixel should have less weight when it is farther away from the line of sight (LOS) path, led to the idea of using a Gaussian distribution for the weight assignment. The general Gaussian function is defined in Equation (18).…”

Section: Gaussian Algorithmmentioning

confidence: 99%

Enhancing indoor radio tomographic imaging based on minimum RF nodes

Abdullah,

Rahiman,

Khalid

et al. 2024

IJEEI

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Uses the attenuation on the links between transceivers to produce an image using Radio Tomographic Imaging (RTI), a network of transceivers, or a Wireless Sensor Network (WSN). Several RTI setups have been constructed as monitoring areas. However, it is observed that most setups have limitations in the number of RF nodes due to a limited number of measurements. However, it is well known that the main difficulty in radio tomographic imaging attributes to the uncertainties in the RSS measurements of transceivers due to multipath effects, especially, when the environment of interest is much cluttered, and requirements on the larger number of nodes for the performance improvement. It is highly remarkable that the motivation of using fewer nodes in this work is to reduce the deployment cost of radio tomographic imaging, slower data collection rates, longer imaging reconstruction times, and bigger sensitivity matricest, this lead author to proposed to design and development of an RTI system with a minimum of 8 RF nodes. The strong and weak received signal strength (RSS) exhibited in the images will be used to assess the effectiveness and accuracy of human sensing localization in a region. The images were reconstructed based on selected image reconstruction algorithms, and they are Linear Back-Projection (LBP), Filtered Back Projection (FBP), Gaussian, Newton's Onestep's Error Reconstruction (NOSER) and Tikhonov Regularization (TR). The reconstructed images will be analysed using the Mean Structural Similarity (MSSIM) index. A comparison between the algorithms mentioned RTI system based on the MSSIM index. NOSER and TR algorithms scored the highest for the MSSIM index overall experiments, and it is the best technique to produce images that appear similar to the original images.

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Section: Gaussian Algorithmmentioning

confidence: 99%