Issue No. 01 - Jan. (2018 vol. 17)
Huseyin Yigitler , Department of Communications and Networking, Aalto University, Aalto, Finland
Riku Jantti , Department of Communications and Networking, Aalto University, Aalto, Finland
Ossi Kaltiokallio , Department of Communications and Networking, Aalto University, Aalto, Finland
Neal Patwari , Department of the Electrical & Computer Engineering, University of Utah, and Xandem Technology LLC, Salt Lake City, UT
Received signal strength based radio tomographic imaging is a popular device-free indoor localization method which reconstructs the spatial loss field of the environment using measurements from a dense wireless network. Existing methods achieve high accuracy localization using a complex system with many sophisticated components. In this work, we propose an alternative and simpler imaging system based on link level occupancy detection. First, we introduce a single-bounce reflection based received signal strength model, which allows relating received signal strength variations to a large region around the link-lines. Then, based on the model, we present methods for all system components including a classifier, a detector, a back-projection based reconstruction algorithm, and a localization method. The introduced system has the following advantages over the other imaging based methods: i) a simple image reconstruction method that is straightforward to implement; ii) significantly lower computational complexity such that no floating point multiplication is required; iii) each link's measured data are compressed to a single bit, providing improved scalability; and iv) physically significant and repeatable parameters. The proposed method is validated using measurement data. Results show that the proposed method achieves the above advantages without loss of accuracy compared to the other available methods.
Detectors, Computational modeling, Monitoring, Image reconstruction, Reconstruction algorithms
H. Yigitler, R. Jantti, O. Kaltiokallio and N. Patwari, "Detector Based Radio Tomographic Imaging," in IEEE Transactions on Mobile Computing, vol. 17, no. 1, pp. 58-71, 2018.