Multi-target Direct Position Determination using Subspace based Compressive Sensing

In this paper, a novel direct position determination (DPD) algorithm was proposed to complement the conventional two-step localization approach, in which the measurement should be associated with the correct target. Considering the usage of array signal model and the inherent sparsity of targets in spatial domain, we combined the MUSIC method and the traditional CS algorithm to localize targets in the region of interest. We first calculated the signal subspace by decomposing the empirical covariance matrix of the received signal, and then took it as the original residual into the greedy iteration, which can greatly decrease the affection of the noise. Moreover, it has been proved that this proposed algorithm can accurately recover a sparse signal with a high probability without knowing the number of targets as a priori, and reduce the cost and complexity of the algorithm by deploying only a small number of base stations. Finally, we conduct a comprehensive set of simulations whose results demonstrate the superiority of our method over the existing algorithms.