Novel measurement matrix optimization for source localization based on compressive sensing

As a promising theory to recover sparse signal from data samples acquired below the Nyquist rate, compressive sensing (CS) has been drawing pervasive interest in the past decade. In this paper, we explore the compressive sensing potentials for the near-field multiple acoustic-source localization. A novel localization scheme is designed by introducing the optimization of the measurement matrix to enforce the restricted isometry property (RIP) and maximize the signal-to-noise ratio (SNR). Monte Carlos simulations have been carried out to demonstrate the effectiveness of our proposed new scheme. Compared to other existing localization techniques, our scheme exhibits superior performances.