Tensor-based multiple denoising via successive spatial smoothing, low-rank approximation and reconstruction for R-D sensor array processing
The parameter estimation problem from noisy signal measurements plays a key role in several practical applications in the array signal processing area ranging from telecommunications and radar to biomedical and acoustics. The performance of parameter estimation techniques is sensitive to the signal-...
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| Veröffentlicht in: | Digital signal processing Jg. 89; S. 1 - 7 |
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| Abstract | The parameter estimation problem from noisy signal measurements plays a key role in several practical applications in the array signal processing area ranging from telecommunications and radar to biomedical and acoustics. The performance of parameter estimation techniques is sensitive to the signal-to-noise ratio (SNR) and severely degrades in noisy scenarios. Classical denoising using SVD low-rank approximation and its tensor counterpart known as higher order SVD (HOSVD) have been widely applied as a preprocessing step to improve the SNR of the received signal. In this paper, we propose the tensor-based multiple denoising (MuDe) approach that successively applies spatial smoothing, denoising and reconstruction to the noisy data. By taking into account the knowledge of the model order and by exploiting subarrays created by the spatial smoothing, we can successively denoise the data by means of HOSVD-based and SVD-based low-rank approximation for tensor and matrix data, respectively. We show that our proposed approach significantly reduces the noise level, allowing a more accurate estimation of parameters compared to state-of-the-art matrix-based and tensor-based techniques without decreasing the sensor array aperture. |
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| AbstractList | The parameter estimation problem from noisy signal measurements plays a key role in several practical applications in the array signal processing area ranging from telecommunications and radar to biomedical and acoustics. The performance of parameter estimation techniques is sensitive to the signal-to-noise ratio (SNR) and severely degrades in noisy scenarios. Classical denoising using SVD low-rank approximation and its tensor counterpart known as higher order SVD (HOSVD) have been widely applied as a preprocessing step to improve the SNR of the received signal. In this paper, we propose the tensor-based multiple denoising (MuDe) approach that successively applies spatial smoothing, denoising and reconstruction to the noisy data. By taking into account the knowledge of the model order and by exploiting subarrays created by the spatial smoothing, we can successively denoise the data by means of HOSVD-based and SVD-based low-rank approximation for tensor and matrix data, respectively. We show that our proposed approach significantly reduces the noise level, allowing a more accurate estimation of parameters compared to state-of-the-art matrix-based and tensor-based techniques without decreasing the sensor array aperture. |
| Author | da Costa, João Paulo C.L. de Almeida, André L.F. Gomes, Paulo R.B. de Sousa, Rafael T. |
| Author_xml | – sequence: 1 givenname: Paulo R.B. surname: Gomes fullname: Gomes, Paulo R.B. email: paulo@gtel.ufc.br organization: Department of Teleinformatics Engineering, Federal University of Ceará, Brazil – sequence: 2 givenname: João Paulo C.L. surname: da Costa fullname: da Costa, João Paulo C.L. email: joaopaulo.dacosta@ene.unb.br organization: Department of Electrical Engineering, University of Brasília, Brazil – sequence: 3 givenname: André L.F. surname: de Almeida fullname: de Almeida, André L.F. email: andre@gtel.ufc.br organization: Department of Teleinformatics Engineering, Federal University of Ceará, Brazil – sequence: 4 givenname: Rafael T. surname: de Sousa fullname: de Sousa, Rafael T. email: desousa@ene.unb.br organization: Department of Electrical Engineering, University of Brasília, Brazil |
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| Cites_doi | 10.1109/TASSP.1985.1164649 10.1109/TSP.2008.917929 10.1109/TGRS.2010.2075937 10.1109/TSP.2017.2652388 10.1016/j.sigpro.2013.04.010 10.1016/j.dsp.2016.12.003 10.1109/TAP.1986.1143830 10.1109/TSP.2010.2043141 10.1109/29.32276 10.1049/el.2015.4380 10.1016/j.dsp.2016.01.003 10.1016/j.media.2014.08.004 10.1109/TSP.2010.2049264 10.1109/TSP.2012.2203814 10.1109/TWC.2016.2604259 10.1016/j.sigpro.2004.11.029 10.1109/78.301848 10.1109/TSP.2010.2058802 10.1137/S0895479896305696 10.1109/MSP.2013.2297439 |
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| Keywords | SVD Multidimensional signal processing Parameter estimation Spatial smoothing Array processing Denoising |
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| SubjectTerms | Array processing Denoising Multidimensional signal processing Parameter estimation Spatial smoothing SVD |
| Title | Tensor-based multiple denoising via successive spatial smoothing, low-rank approximation and reconstruction for R-D sensor array processing |
| URI | https://dx.doi.org/10.1016/j.dsp.2019.01.005 |
| Volume | 89 |
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