Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: An efficient implementation of iterative adaptive approach for source localization | Li et al. EURASIP Journal on Advances in Signal Processing 2012 2012 7 http content 2012 1 7 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access An efficient implementation of iterative adaptive approach for source localization Gang Li 1 Hao Zhang1 Xiqin Wang1 and Xiang-Gen Xia2 Abstract The iterative adaptive approach IAA can achieve accurate source localization with single snapshot and therefore it has attracted significant interest in various applications. In the original IAA the optimal filter is performed for every scanning angle grid in each iteration which may cause the slow convergence and disturb the spatial estimates on the impinging angles of sources. In this article we propose an efficient implementation of IAA EIAA by modifying the use of the optimal filtering . in each iteration of EIAA the optimal filter is only utilized to estimate the spatial components likely corresponding to the impinging angles of sources and other spatial components corresponding to the noise are updated by the simple correlation of the basis matrix with the residue. Simulation results show that in comparison with IAA EIAA has significant higher computational efficiency and comparable accuracy of source angle and power estimation. Keywords sparse recovery iterative adaptive approach source localization 1. Introduction Source localization is a fundamental problem in a wide range of applications including communications radar and acoustics and many algorithms have been presented in the literature during recent decades. The Fourierbased algorithms suffer from the low resolution and the high sidelobes. Some methods based on subspace processing . Capon beamforming 1 MUSIC 2 ESPRIT 3 and other subspace-based algorithms 4 5 provide super-resolution for uncorrelated sources with sufficient number of snapshots. However in the case of few snapshots the performances of these subspacebased methods will degrade .
