Interlaced Sampling Corrupted by Noise
Abstract The problem addressed in this paper is the reconstruction of a continuous-time stationary process from noisy interlaced sampled observations. This work extends the one of Yen who gave an exact reconstruction of a process or a function with spectral support within (−Kπ, Kπ) subjected to inte...
Ausführliche Beschreibung
Gespeichert in:
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Lacaze, B. [verfasserIn] Mailhes, C. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2002 |
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| Schlagwörter: |
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| Anmerkung: |
© SIP 2002 |
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| Übergeordnetes Werk: |
Enthalten in: Sampling theory, signal processing, and data analysis - [Cham] : Birkhäuser, 2021, 1(2002), 3 vom: 01. Sept., Seite 185-205 |
|---|---|
| Übergeordnetes Werk: |
volume:1 ; year:2002 ; number:3 ; day:01 ; month:09 ; pages:185-205 |
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| DOI / URN: |
10.1007/BF03549378 |
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Abstract The problem addressed in this paper is the reconstruction of a continuous-time stationary process from noisy interlaced sampled observations. This work extends the one of Yen who gave an exact reconstruction of a process or a function with spectral support within (−Kπ, Kπ) subjected to interlaced sampling i.e. when sampling times are such that tkn = n + ak, n ∈ Z, k = 1, …, K. In this paper, we derive the linear mean square estimator (LMSE) of a continuous-time process based on the observations of noisy interlaced samples. Two different cases are studied. The first one deals with noise samples coming from a unique stationary process. In the second one, K uncorrelated noises with different spectra are considered. In both cases, the specific sampling leads to technics linked to cyclostationarity properties. © SIP 2002 |
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Abstract The problem addressed in this paper is the reconstruction of a continuous-time stationary process from noisy interlaced sampled observations. This work extends the one of Yen who gave an exact reconstruction of a process or a function with spectral support within (−Kπ, Kπ) subjected to interlaced sampling i.e. when sampling times are such that tkn = n + ak, n ∈ Z, k = 1, …, K. In this paper, we derive the linear mean square estimator (LMSE) of a continuous-time process based on the observations of noisy interlaced samples. Two different cases are studied. The first one deals with noise samples coming from a unique stationary process. In the second one, K uncorrelated noises with different spectra are considered. In both cases, the specific sampling leads to technics linked to cyclostationarity properties. © SIP 2002 |
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Abstract The problem addressed in this paper is the reconstruction of a continuous-time stationary process from noisy interlaced sampled observations. This work extends the one of Yen who gave an exact reconstruction of a process or a function with spectral support within (−Kπ, Kπ) subjected to interlaced sampling i.e. when sampling times are such that tkn = n + ak, n ∈ Z, k = 1, …, K. In this paper, we derive the linear mean square estimator (LMSE) of a continuous-time process based on the observations of noisy interlaced samples. Two different cases are studied. The first one deals with noise samples coming from a unique stationary process. In the second one, K uncorrelated noises with different spectra are considered. In both cases, the specific sampling leads to technics linked to cyclostationarity properties. © SIP 2002 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR043387136</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220112055039.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210303s2002 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/BF03549378</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR043387136</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)BF03549378-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lacaze, B.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Interlaced Sampling Corrupted by Noise</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2002</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© SIP 2002</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The problem addressed in this paper is the reconstruction of a continuous-time stationary process from noisy interlaced sampled observations. This work extends the one of Yen who gave an exact reconstruction of a process or a function with spectral support within (−Kπ, Kπ) subjected to interlaced sampling i.e. when sampling times are such that tkn = n + ak, n ∈ Z, k = 1, …, K. In this paper, we derive the linear mean square estimator (LMSE) of a continuous-time process based on the observations of noisy interlaced samples. Two different cases are studied. The first one deals with noise samples coming from a unique stationary process. In the second one, K uncorrelated noises with different spectra are considered. 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