Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
Direction-of-arrival (DoA) estimation of electromagnetic (EM) waves impinging on a spherical antenna array in short time windows is examined in this paper. Reflected EM signals due to non-line-of-sight propagation measured with a spherical antenna array can be coherent and/or highly correlated in a...
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ftmdpi:oai:mdpi.com:/2079-9292/11/2/208/ 2023-05-15T16:01:57+02:00 Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array Oluwole John Famoriji; Thokozani Shongwe 2022-01-10 application/pdf https://doi.org/10.3390/electronics11020208 EN eng Multidisciplinary Digital Publishing Institute Microwave and Wireless Communications https://dx.doi.org/10.3390/electronics11020208 https://creativecommons.org/licenses/by/4.0/ CC-BY Electronics; Volume 11; Issue 2; Pages: 208 Cramér–Rao lower bound (CRLB) direction-of-arrival (DoA) estimation maximum likelihood (ML) spherical antenna array (SAA) root-mean-square error (RMSE) Text 2022 ftmdpi https://doi.org/10.3390/electronics11020208 2022-01-17T00:36:27Z Direction-of-arrival (DoA) estimation of electromagnetic (EM) waves impinging on a spherical antenna array in short time windows is examined in this paper. Reflected EM signals due to non-line-of-sight propagation measured with a spherical antenna array can be coherent and/or highly correlated in a snapshot. This makes spectral-based methods inefficient. Spectral methods, such as maximum likelihood (ML) methods, multiple signal classification (MUSIC), and beamforming methods, are theoretically and systematically investigated in this study. MUSIC is an approach used for frequency estimation and radio direction finding, ML is a technique used for estimating the parameters of an assumed probability distribution for given observed data, and PWD applies a Fourier transform to the capture response and produces them in the frequency domain. Although they have been previously adapted and used to estimate DoA of EM signals impinging on linear and planar antenna array configurations, this paper investigates their suitability and effectiveness for a spherical antenna array. Various computer simulations were conducted, and plots of root-mean-square error (RMSE) against the square root of the Cramér–Rao lower bound (CRLB) were generated and used to evaluate the performance of each method. Numerical experiments and results from measured data show the degree of appropriateness and efficiency of each method. For instance, the techniques exhibit identical performance to that in the wideband scenario when the frequency f = 8 GHz, f = 16 GHz, and f = 32 GHz, but f = 16 GHz performs best. This indicates that the difference between the covariance matrix of the signal is coherent and that the steering vectors of signals impinging from that angle are small. MUSIC and PWD share the same problems in the single-frequency scenario as in the wideband scenario when the delay sample d = 0. Consequently, the DoA estimation obtained with ML techniques is more suitable, less biased, and more robust against noise than beamforming and MUSIC techniques. In addition, deterministic ML (DML) and weighted subspace fitting (WSF) techniques show better DoA estimation performance than the stochastic ML (SML) technique. For a large number of snapshots, WSF is a better choice because it is more computationally efficient than DML. Finally, the results obtained indicate that WSF and ML methods perform better than MUSIC and PWD for the coherent or partially correlated signals studied. Text DML MDPI Open Access Publishing Electronics 11 2 208 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
Cramér–Rao lower bound (CRLB) direction-of-arrival (DoA) estimation maximum likelihood (ML) spherical antenna array (SAA) root-mean-square error (RMSE) |
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Cramér–Rao lower bound (CRLB) direction-of-arrival (DoA) estimation maximum likelihood (ML) spherical antenna array (SAA) root-mean-square error (RMSE) Oluwole John Famoriji; Thokozani Shongwe Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array |
topic_facet |
Cramér–Rao lower bound (CRLB) direction-of-arrival (DoA) estimation maximum likelihood (ML) spherical antenna array (SAA) root-mean-square error (RMSE) |
description |
Direction-of-arrival (DoA) estimation of electromagnetic (EM) waves impinging on a spherical antenna array in short time windows is examined in this paper. Reflected EM signals due to non-line-of-sight propagation measured with a spherical antenna array can be coherent and/or highly correlated in a snapshot. This makes spectral-based methods inefficient. Spectral methods, such as maximum likelihood (ML) methods, multiple signal classification (MUSIC), and beamforming methods, are theoretically and systematically investigated in this study. MUSIC is an approach used for frequency estimation and radio direction finding, ML is a technique used for estimating the parameters of an assumed probability distribution for given observed data, and PWD applies a Fourier transform to the capture response and produces them in the frequency domain. Although they have been previously adapted and used to estimate DoA of EM signals impinging on linear and planar antenna array configurations, this paper investigates their suitability and effectiveness for a spherical antenna array. Various computer simulations were conducted, and plots of root-mean-square error (RMSE) against the square root of the Cramér–Rao lower bound (CRLB) were generated and used to evaluate the performance of each method. Numerical experiments and results from measured data show the degree of appropriateness and efficiency of each method. For instance, the techniques exhibit identical performance to that in the wideband scenario when the frequency f = 8 GHz, f = 16 GHz, and f = 32 GHz, but f = 16 GHz performs best. This indicates that the difference between the covariance matrix of the signal is coherent and that the steering vectors of signals impinging from that angle are small. MUSIC and PWD share the same problems in the single-frequency scenario as in the wideband scenario when the delay sample d = 0. Consequently, the DoA estimation obtained with ML techniques is more suitable, less biased, and more robust against noise than beamforming and MUSIC techniques. In addition, deterministic ML (DML) and weighted subspace fitting (WSF) techniques show better DoA estimation performance than the stochastic ML (SML) technique. For a large number of snapshots, WSF is a better choice because it is more computationally efficient than DML. Finally, the results obtained indicate that WSF and ML methods perform better than MUSIC and PWD for the coherent or partially correlated signals studied. |
format |
Text |
author |
Oluwole John Famoriji; Thokozani Shongwe |
author_facet |
Oluwole John Famoriji; Thokozani Shongwe |
author_sort |
Oluwole John Famoriji; Thokozani Shongwe |
title |
Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array |
title_short |
Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array |
title_full |
Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array |
title_fullStr |
Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array |
title_full_unstemmed |
Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array |
title_sort |
critical review of basic methods on doa estimation of em waves impinging a spherical antenna array |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/electronics11020208 |
genre |
DML |
genre_facet |
DML |
op_source |
Electronics; Volume 11; Issue 2; Pages: 208 |
op_relation |
Microwave and Wireless Communications https://dx.doi.org/10.3390/electronics11020208 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/electronics11020208 |
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Electronics |
container_volume |
11 |
container_issue |
2 |
container_start_page |
208 |
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1766397620636352512 |