Adaptive filtering algorithm for direction-of-arrival (DOA) estimation with small snapshots
Autor: | Guan Gui, Beiyi Liu, Li Xu, Shin-ya Matsushita |
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Rok vydání: | 2019 |
Předmět: |
Computer science
Applied Mathematics MIMO Initialization Direction of arrival 020206 networking & telecommunications 02 engineering and technology Sparse approximation Matching pursuit Least mean squares filter Compressed sensing Computational Theory and Mathematics Artificial Intelligence Robustness (computer science) Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Electrical and Electronic Engineering Statistics Probability and Uncertainty Algorithm |
Zdroj: | Digital Signal Processing. 94:84-95 |
ISSN: | 1051-2004 |
Popis: | The direction-of-arrival (DOA) estimation problem with a few noisy snapshots can be formulated as a problem of finding a joint sparse representation of multiple measurement vectors (MMV), and some algorithms based on compressive sensing (CS), such as the joint l 0 approximation DOA (JLZA-DOA) and Multiple Snapshot Matching Pursuit Direction of Arrival (MSMPDOA) algorithms, have recently been proposed for solving this problem. Compared with the conventional DOA methods, the CS-based methods can achieve super-resolution by using only small number of snapshots, without the necessity of an accurate initialization, with small sensitivity to the correlation of the source signals. However, these CS-based algorithms usually do not work well in low signal-noise ratio (SNR) environment. In addition, the increased number of sensors in massive multiple-input-multiple-output (MIMO) systems lead to a huge matrix, and the matrix inversion operation in each iteration of the CS-based algorithm results in a relatively high computational cost. The purpose of this paper is to propose a novel adaptive filtering algorithm, i.e., the l 2 , 0 -least mean square ( l 2 , 0 -LMS) algorithm, which can be viewed as a generalization of the l 0 -LMS algorithm for single measurement vector (SMV) problem. Our proposed algorithm incorporates a mixed norm ( l 2 , 0 -norm) to treat the joint sparsity and inherits the robustness against noise and the low complexity of the l 0 -LMS algorithm, and can thus work well for massive MIMO systems. Numerical experiments demonstrate that the proposed algorithm can achieve much better estimation performance with a lower computational cost than the existing ones. |
Databáze: | OpenAIRE |
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