Adaptive filtering algorithm for direction-of-arrival (DOA) estimation with small snapshots
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 ℓ0 approximation DOA (JLZA-DOA) and Mul...
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| Vydané v: | Digital signal processing Ročník 94; s. 84 - 95 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.11.2019
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| ISSN: | 1051-2004, 1095-4333 |
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| Abstract | 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 ℓ0 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 ℓ2,0-least mean square (ℓ2,0-LMS) algorithm, which can be viewed as a generalization of the ℓ0-LMS algorithm for single measurement vector (SMV) problem. Our proposed algorithm incorporates a mixed norm (ℓ2,0-norm) to treat the joint sparsity and inherits the robustness against noise and the low complexity of the ℓ0-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. |
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| AbstractList | 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 ℓ0 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 ℓ2,0-least mean square (ℓ2,0-LMS) algorithm, which can be viewed as a generalization of the ℓ0-LMS algorithm for single measurement vector (SMV) problem. Our proposed algorithm incorporates a mixed norm (ℓ2,0-norm) to treat the joint sparsity and inherits the robustness against noise and the low complexity of the ℓ0-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. |
| Author | Xu, Li Matsushita, Shin-ya Liu, Beiyi Gui, Guan |
| Author_xml | – sequence: 1 givenname: Beiyi orcidid: 0000-0002-0038-8039 surname: Liu fullname: Liu, Beiyi email: D19S001@akita-pu.ac.jp organization: Department of Intelligent Mechatronics, Akita Prefectural University, Akita, 0150055, Japan – sequence: 2 givenname: Guan orcidid: 0000-0003-3888-2881 surname: Gui fullname: Gui, Guan email: guiguan@njupt.edu.cn organization: College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China – sequence: 3 givenname: Shin-ya surname: Matsushita fullname: Matsushita, Shin-ya email: matsushita@akita-pu.ac.jp organization: Department of Intelligent Mechatronics, Akita Prefectural University, Akita, 0150055, Japan – sequence: 4 givenname: Li orcidid: 0000-0002-1875-771X surname: Xu fullname: Xu, Li email: xuli@akita-pu.ac.jp organization: Department of Intelligent Mechatronics, Akita Prefectural University, Akita, 0150055, Japan |
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| Keywords | Massive MIMO system Direction-of-arrival estimation Sensor array processing Mixed-norm Adaptive filter Sparse signal representation |
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