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| | ONLINE | ISSN | : | 1745-1361 | | PRINT | ISSN | : | 0916-8532 |
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| | IEICE Transactions on Information and Systems |
| E91.D (2008) , No. 5 pp.1577-1580 |
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 | Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise
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| | 1) Department of Computer Science, Tokyo Institute of Technology
2) Fraunhofer FIRST. IDA
3) Department of Computer Science, Technical University of Berlin
4) The Institute of Electronics, Information and Communication Engineers |
| (Manuscript received: July 20, 2007)
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| | SUMMARY Obtaining the best linear unbiased estimator (BLUE) of noisy signals is a traditional but powerful approach to noise reduction. Explicitly computing the BLUE usually requires the prior knowledge of the noise covariance matrix and the subspace to which the true signal belongs. However, such prior knowledge is often unavailable in reality, which prevents us from applying the BLUE to real-world problems. To cope with this problem, we give a practical procedure for approximating the BLUE without such prior knowledge. Our additional assumption is that the true signal follows a non-Gaussian distribution while the noise is Gaussian. |
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| To cite this article: | | Masashi SUGIYAMA, Motoaki KAWANABE, Gilles BLANCHARD and Klaus-Robert MÜLLER, “Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise”, IEICE Trans. Inf. & Syst., Vol. E91.D, No. 5, pp.1577-1580, 2008 . | |
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| doi:10.1093/ietisy/e91-d.5.1577 | | JOI JST.JSTAGE/transinf/E91.D.1577 |
| Copyright (c) 2010 The Institute of Electronics, Information and Communication Engineers |
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