Sparse localization on the basis of Schmidt orthonormalization in wireless sensor networks

To improve the localization accuracy of a node in the wireless sensor network with a mobile beacon node, a sparse localization algorithm using Schmidt orthonormalization (SLSO) was proposed. With the SLSO, the node localization problem was converted to a reconstruction problem of the sparse signal by gridding the sensing area, and a new observation matrix which is able to effectively satisfy the restricted isometry property (RIP) was obtained by Schmidt orthonormalization. To solve the problem of the sparse signal being approximately sparse in the model, a centroid algorithm was adopted to improve the localization accuracy. The experiment results show that, compared with MAP algorithms, SLSO has better localization accuracy, and requires less broadcasting times.