Imaging and tracking performance suffers from the mismatch between the model and the measurements in an adaptive radio tomographic imaging system. In this paper, a model-based approach is reviewed and a new adaptive elliptical weighting model is proposed, in which the coverage of ellipse and the voxels weightings can adaptively match the actual environments, and the Savitzky–Golay smoothing filter is presented to eliminate the influence of measurement noise and multipath interference. In our proposed model, the optimal coverage of ellipse and weightings can be obtained from voxel weightings distribution inside the ellipse and pseudo-position area and trailing phenomenon. Finally, the development efforts are evaluated and validated with real experiments conducted in indoor environments for a moving target. The results have shown that the proposed algorithm can improve the accuracy of image and location estimates compared with the normalized weight model and the const-eccentricity weight model.
ENHANCED RADIO TOMOGRAPHIC IMAGING METHOD FOR DEVICE-FREE LOCALIZATION USING A GRADUAL-CHANGING WEIGHT MODEL
