Radio tomographic imaging (RTI) based on received signal strength (RSS) measurements has emerged to be one of promising and effective technologies to reveal the obstacle in the resulting attenuation image. Suffering from the coarse elliptical weighting model and the multipath interference, the traditional RTI is unable to accurately map the obstacle, especially in outline recognition. In this paper, we demonstrate an improved RTI method for obstacle mapping. Since the detail mapping requires radio propagation more concentrated, we apply the inverse area elliptical propagation model to describe the RSS attenuation occurred in the propagation path across the obstacle. Moreover, introducing the directional information into the spatial correlation matrix, we enhance the imaging accuracy by a modified Tikhonov regularizer with a non-negative constraint. Field mapping experiments using directional antennas are performed with obstacles built of different materials. Experimental results suggest that the obstacle mapping quality of the improved method is better than that of the traditional RTI method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Experimental Verification: Enabling Obstacle Mapping Based On Radio Tomographic Imaging


    Contributors:
    Xu, Shengxin (author) / Liu, Heng (author) / Gao, Fei (author) / Chen, Sisi (author)


    Publication date :

    2018-08-01


    Size :

    5672280 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    RADIO FREQUENCY-BASED OBSTACLE AVOIDANCE

    LAU ERIE LAI HAR / DOWLATKHAH SANGAR / GREAVES BRIAN et al. | European Patent Office | 2018

    Free access

    Radio frequency-based obstacle avoidance

    LAU ERIE LAI HAR / DOWLATKHAH SANGAR / GREAVES BRIAN et al. | European Patent Office | 2019

    Free access

    Obstacle detection and mapping

    RAICHELGAUZ IGAL / ODINAEV KARINA | European Patent Office | 2022

    Free access

    Radio Frequency Tomographic Reconstruction Based on Convolutional Neural Networks

    Li, Jia / Ewing, Robert L. / Shen, Xiaoping | IEEE | 2018


    MEMS tomographic imaging system simulation

    Ciccarelli, S.M. / Amuso, V.J. | IEEE | 2009