The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Blind beamforming for non-gaussian signals
Open accessAbstract
The paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resort to their hypothesised value. By using estimates of the directional vectors obtained via blind identification, i.e. without knowing the array manifold, beamforming is made robust with respect to array deformations, distortion of the wave front, pointing errors etc., so that neither array calibration nor physical modelling is necessary. Rather suprisingly, ‘blind beamformers’ may outperform ‘informed beamformers’ in a plausible range of parameters, even when the array is perfectly known to the informed beamformer. The key assumption on which blind identification relies is the statistical independence of the sources, which is exploited using fourth-order cumulants. A computationally efficient technique is presented for the blind estimation of directional vectors, based on joint diagonalisation of fourth-order cumulant matrices; its implementation is described, and its performance is investigated by numerical experiments.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Beamforming: a versatile approach to spatial filtering
Van Veen, B. D., & Buckley, K. M. (1988) · IEEE ASSP Magazine · doi:10.1109/53.665 · Cited by 3,901
Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems
Godard, D. (1980) · IEEE Transactions on Communications · doi:10.1109/tcom.1980.1094608 · Cited by 2,530
New criteria for blind deconvolution of nonminimum phase systems (channels)
Shalvi, O., & Weinstein, E. (1990) · IEEE Transactions on Information Theory · doi:10.1109/18.52478 · Cited by 863
Source separation using higher order moments
Cardoso, J.-F. (2003) · International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1989.266878 · Cited by 556
Self-citationON MINIMUM ENTROPY DECONVOLUTION
Donoho, D. (1981) · Applied Time Series Analysis II · doi:10.1016/b978-0-12-256420-8.50024-1 · Cited by 472
Cardoso, J.-F. (1991) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1991.150113 · Cited by 161
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Cited by
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Kraskov, A., Stögbauer, H., & Grassberger, P. (2004) · Physical Review E · doi:10.1103/physreve.69.066138 · Cited by 3,512
A blind source separation technique using second-order statistics
Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., & Moulines, E. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.554307 · Cited by 2,775
Self-citationLee, T.-W., Girolami, M., & Sejnowski, T. J. (1999) · Neural Computation · doi:10.1162/089976699300016719 · Cited by 1,967
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
Lahat, D., Adali, T., & Jutten, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2460697 · Cited by 1,296
Metadata
- Authors
- Cardoso, J. F., & Souloumiac, A.
- Venue
- IEE Proceedings F Radar and Signal Processing
- Published
- 1993
- Volume
- 140
- Issue
- 6
- Pages
- 362
- ISSN
- 0956-375X (ISSN-L)
- OpenAlex
- W2124757684
- Cited by
- 2,738
- References
- 7
- FWCI
- 13.09
- Type
- journal article
- Language
- en
- Open access
- green
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- Cardoso, J. F., & Souloumiac, A. (1993). Blind beamforming for non-gaussian signals. IEE Proceedings F Radar and Signal Processing. https://doi.org/10.1049/ip-f-2.1993.0054
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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