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

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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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Dates and versions

hal-01823750 , version 1 (29-06-2018)

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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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