Radio Tomographic imaging (RTI) is an exciting new field that utilizes a sensor network of a large number of relatively simple radio nodes for inverse imaging, utilizing similar mathematical algorithms to those used in medical imaging. Previous work in this field has almost exclusively focused on device-free object location and tracking. In this thesis, the application of RTI to propagation problems will be studied-- specifically using RTI to measure the strength and location of attenuating objects in an area of interest, then using this knowledge of the shadowing present in an area for radio coverage prediction.
In addition to radio coverage prediction, RTI can be used to improve the quality of RSS-based position location estimates. Because the traditional failing of RSS-based multilateration is ranging error due to attenuating objects, RTI has great potential for improving the accuracy of these estimates if shadowing objects are accounted for.
In this thesis, these two problems will primarily be studied. A comparison with other inverse imaging, remote sensing, and propagation modeling techniques of interest will be given, as well as a description of the mathematical theory used for tomographic image reconstruction. Proof-of-concept of the efficacy of applying RTI to position location will be given by computer simulation, and then physical experiments with an RTI network consisting of 28 Zigbee radio sensors will be used to verify the validity of these assertions. It will be shown in this thesis that RTI does provide noticeable improvement in RSS-based position location accuracy in cluttered environments, and it produces much more accurate RSS estimates than a standard exponential path-loss model is able to provide.
