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, i.e., 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.
Highlights
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
We propose an efficient implementation of iterative adaptive approach (IAA) (EIAA) by modifying the use of the optimal filtering, i.e., in each iteration, the optimal filter is only utilized to estimate the spatial components likely corresponding to the actual signal sources, and other spatial components corresponding to the noise are updated by the simple correlation of the basis matrix with the residue
It is noted that the way of the computational burden reduction in this article is different from [, ]: we focus on reducing the number of running optimal filtering procedures, while [, ] focus on improving the computational efficiency of the optimal filtering procedure
Summary
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 main difference between the proposed EIAA and the original IAA lies in the estimation of spatial components that are outside the actual source location set. The spatial components corresponding to the actual source locations are updated by optimal filtering, and other spatial components corresponding to the noise are updated by simple correlation of the columns of basis matrix with the residue This implies that the excessive estimation of noise components is avoided. Compared with the original IAA, EIAA can significantly reduce the computational burden thanks to the following facts: ( ) In each iteration, the required times of optimal filtering procedure is equal to the number of the selected principle components in step (b) of Table. ; way, the number of the selected principle components, i.e., the required times of optimal filtering procedure, is usually much smaller than K , which is guaranteed by the prior assumption of sparse signal property. ( ) The relaxation of the estimation of spatial components corresponding to noise leads to stable and fast convergence
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