Delaunay triangulation and mesh grid combining algorithm for multiple targets localization using compressive sensing

Introducing compressive sensing theory to solve multiple targets localization problem is a promising method by only considering received signal's power. In previous researches, the targets are assumed to be positioned into an regular mesh grid and restoration model according to compressive underdetermined equation is built to recover the positions of unknown targets. However, it is inconvenient to arrange sensors on grids in practice and the recovered locations were bound to grid points that may deviate the target positions comparing to the real ones. In this paper, we propose a Delaunay triangulation grid generation algorithm through randomly deployed sensors' positions. Moreover, a sparse position vector diversity combining algorithm, based on OMP, is also been proposed. It lowers the possibility of the emergence of the false locations and greatly improves the accuracy of targets' positions.