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SUBSPACE SIGNAL-TO-NOISE RATIO MAXIMIZATION: THE CONSTRAINED STOCHASTIC MATCHED FILTER

Abstract : This paper deals with signal-to-noise ratio (SNR) max-imization in a subspace. This criterion can be applied to estimation or detection of a stochastic signals embedded in additive noise. Both signal and noise are considered to be realizations of random functions whose statistics are supposed known up to second-order. The method proposed leads to a filter we call " constrained stochastic matched filter " (CSMF), optimal in the sense that it maximizes the SNR in a subspace whose dimension is chosen a priori. It is an extension of the stochastic matched filter (SMF), itself an extension of the matched filter.
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https://hal-amu.archives-ouvertes.fr/hal-01823750
Contributor : Bruno Borloz <>
Submitted on : Friday, June 29, 2018 - 9:34:29 AM
Last modification on : Wednesday, September 25, 2019 - 12:08:03 PM
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Bruno Borloz, Bernard Xerri. SUBSPACE SIGNAL-TO-NOISE RATIO MAXIMIZATION: THE CONSTRAINED STOCHASTIC MATCHED FILTER. Eighth International Symposium on Signal Processing and Its Applications, 2005., 2005, Sydney, France. ⟨10.1109/ISSPA.2005.1581043⟩. ⟨hal-01823750⟩

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