Coprime beamforming: fast estimation of more sources than sensors
In radar, the minimisation of redundancies within the sensing array can lead to significant hardware computational savings. Arrays with a coprime-pair configuration enjoy increased degrees of freedom and can detect more sources than sensors. To achieve this, a virtual array (VA) consisting of the fu...
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| Published in | IET radar, sonar & navigation Vol. 13; no. 11; pp. 1956 - 1962 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
The Institution of Engineering and Technology
01.11.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-8784 1751-8792 1751-8792 |
| DOI | 10.1049/iet-rsn.2018.5647 |
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| Abstract | In radar, the minimisation of redundancies within the sensing array can lead to significant hardware computational savings. Arrays with a coprime-pair configuration enjoy increased degrees of freedom and can detect more sources than sensors. To achieve this, a virtual array (VA) consisting of the full complement of lags is usually constructed and subspace-based algorithms are then employed to obtain the direction-of-arrival (DOA) or frequency estimates. However, the application of the subspace techniques to the VA incurs a significant computational cost and requires spatial smoothing. The authors propose and analyse the application of the fast iterative interpolated beamformer (FIIB) to the coprime DOA estimation problem. The FIIB enjoys a computational complexity of the same order as the fast Fourier transform and does not require spatial smoothing. They consider two implementations that construct the VA differently with the first selecting a single estimate for each lag and the other employing averaged values of the lag estimates. They present a comprehensive study of the estimation performance as a function of signal-to-noise ratio, number of snapshots and source separation. The results clearly show that the FIIB delivers high-fidelity frequency estimates that consistently outperform the high-resolution subspace-based methods. |
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| AbstractList | In radar, the minimisation of redundancies within the sensing array can lead to significant hardware computational savings. Arrays with a coprime‐pair configuration enjoy increased degrees of freedom and can detect more sources than sensors. To achieve this, a virtual array (VA) consisting of the full complement of lags is usually constructed and subspace‐based algorithms are then employed to obtain the direction‐of‐arrival (DOA) or frequency estimates. However, the application of the subspace techniques to the VA incurs a significant computational cost and requires spatial smoothing. The authors propose and analyse the application of the fast iterative interpolated beamformer (FIIB) to the coprime DOA estimation problem. The FIIB enjoys a computational complexity of the same order as the fast Fourier transform and does not require spatial smoothing. They consider two implementations that construct the VA differently with the first selecting a single estimate for each lag and the other employing averaged values of the lag estimates. They present a comprehensive study of the estimation performance as a function of signal‐to‐noise ratio, number of snapshots and source separation. The results clearly show that the FIIB delivers high‐fidelity frequency estimates that consistently outperform the high‐resolution subspace‐based methods. |
| Author | Baxter, William Nosrati, Hamed Aboutanios, Elias |
| Author_xml | – sequence: 1 givenname: William orcidid: 0000-0001-8259-0654 surname: Baxter fullname: Baxter, William email: w.baxter@unsw.edu.au organization: 1School of Electrical Engineering and Telecommunications, University of New South Wales Australia, Building G17 UNSW 2052, Australia – sequence: 2 givenname: Hamed surname: Nosrati fullname: Nosrati, Hamed organization: 2Data61, CSIRO, Australia – sequence: 3 givenname: Elias surname: Aboutanios fullname: Aboutanios, Elias organization: 1School of Electrical Engineering and Telecommunications, University of New South Wales Australia, Building G17 UNSW 2052, Australia |
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| CitedBy_id | crossref_primary_10_1049_rsn2_12211 crossref_primary_10_1049_rsn2_12015 crossref_primary_10_1016_j_sigpro_2021_108284 crossref_primary_10_1007_s11045_021_00787_8 crossref_primary_10_1016_j_dsp_2021_103332 crossref_primary_10_1155_2022_1442459 |
| Cites_doi | 10.1109/TSP.2010.2089682 10.1016/j.apacoust.2013.01.005 10.1109/LSP.2015.2409153 10.1109/29.61541 10.1002/0471221104 10.1109/TSP.2013.2263502 10.1109/78.917800 10.5194/ars-8-251-2010 10.1109/29.32276 10.1186/1687-6180-2014-120 10.1109/78.143447 10.1109/TAP.1968.1139138 10.1109/TSP.2014.2354316 10.1109/DSP-SPE.2011.5739227 10.1049/ip-h-2.1991.0074 10.1016/j.sigpro.2013.03.009 10.5121/sipij.2015.6602 10.1016/j.dsp.2016.04.011 10.1109/TSP.2016.2626255 10.1109/RADAR.2018.8557304 10.1109/TSP.2015.2393838 10.1109/TSP.2005.850882 10.1109/79.526899 10.7305/automatika.54-2.131 |
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| Keywords | fast Fourier transforms iterative methods single estimate coprime-pair configuration subspace techniques virtual array direction-of-arrival estimation high-resolution subspace-based methods DOA array signal processing lag estimates computational cost degrees of freedom subspace-based algorithms VA estimation performance fast estimation fast Fourier transform source separation spatial smoothing interpolation fast iterative interpolated beamformer coprime beamforming sensing array FIIB significant hardware computational savings computational complexity high-fidelity frequency estimates |
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| Snippet | In radar, the minimisation of redundancies within the sensing array can lead to significant hardware computational savings. Arrays with a coprime-pair... In radar, the minimisation of redundancies within the sensing array can lead to significant hardware computational savings. Arrays with a coprime‐pair... |
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| StartPage | 1956 |
| SubjectTerms | array signal processing computational complexity computational cost coprime beamforming coprime‐pair configuration degrees of freedom direction‐of‐arrival estimation DOA estimation performance fast estimation fast Fourier transform fast Fourier transforms fast iterative interpolated beamformer FIIB high‐fidelity frequency estimates high‐resolution subspace‐based methods interpolation iterative methods lag estimates Research Article sensing array significant hardware computational savings single estimate source separation spatial smoothing subspace techniques subspace‐based algorithms virtual array |
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| Title | Coprime beamforming: fast estimation of more sources than sensors |
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