Many applications provided by wireless sensor networks rely heavily on the location information of the monitored targets. Since the number of targets in the region of interest is limited, localization benefits from compressive sensing, sampling number can be greatly reduced. Despite many compressive sensing–based localization methods proposed, existing solutions are based on the assumption that all targets fall on a sampled and fixed grid, performing poorly when there are targets deviating from the grid. To address such a problem, in this article, we propose a dictionary refinement algorithm where the grid is iteratively adjusted to alleviate the deviation. In each iteration, the representation coefficient and the grid parameters are updated in turn. After several iterations, the measurements can be sparsely represented by the representation coefficient which indicates the number and locations of multiple targets. Extensive simulation results show that the proposed dictionary refinement algorithm achieves more accurate counting and localization compared to the state-of-the-art compressive sensing reconstruction algorithms.
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