Parameter estimation of cyclostationary AM time series with application to missing observations
Time series with systematic misses occur often in practice and can be modeled as amplitude modulated ARMA processes. With this as a motivating application, modeling of cyclostationary amplitude modulated time series is addressed in the paper. Assuming that the modulating sequence is (almost) periodi...
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| Veröffentlicht in: | IEEE transactions on signal processing 1994-09, Vol.42 (9), p.2408-2419 |
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| container_title | IEEE transactions on signal processing |
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| creator | Giannakis, G.B. Guotong Zhou |
| description | Time series with systematic misses occur often in practice and can be modeled as amplitude modulated ARMA processes. With this as a motivating application, modeling of cyclostationary amplitude modulated time series is addressed in the paper. Assuming that the modulating sequence is (almost) periodic, parameter estimation algorithms are developed based on second- and higher order cumulants of the resulting cyclostationary observations, which may be corrupted by any additive stationary noise of unknown covariance. If unknown, the modulating sequence can be recovered even in the presence of additive (perhaps nonstationary and colored) Gaussian, or any symmetrically distributed, noise. If the ARMA process is nonGaussian, cyclic cumulants of order greater than three can identify (non)causal and (non)minimum phase models from partial noisy data. Simulation experiments corroborate the theoretical results.< > |
| doi_str_mv | 10.1109/78.317862 |
| format | Article |
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With this as a motivating application, modeling of cyclostationary amplitude modulated time series is addressed in the paper. Assuming that the modulating sequence is (almost) periodic, parameter estimation algorithms are developed based on second- and higher order cumulants of the resulting cyclostationary observations, which may be corrupted by any additive stationary noise of unknown covariance. If unknown, the modulating sequence can be recovered even in the presence of additive (perhaps nonstationary and colored) Gaussian, or any symmetrically distributed, noise. If the ARMA process is nonGaussian, cyclic cumulants of order greater than three can identify (non)causal and (non)minimum phase models from partial noisy data. Simulation experiments corroborate the theoretical results.< ></description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/78.317862</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Additive noise ; Amplitude modulation ; Applied sciences ; Colored noise ; Detection, estimation, filtering, equalization, prediction ; Exact sciences and technology ; Gaussian noise ; Information, signal and communications theory ; Parameter estimation ; Phase noise ; Radar applications ; Signal and communications theory ; Signal, noise ; Telecommunications and information theory ; Time series analysis ; Underwater acoustics ; Underwater communication</subject><ispartof>IEEE transactions on signal processing, 1994-09, Vol.42 (9), p.2408-2419</ispartof><rights>1995 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c306t-92e3067f49ecb06563c4f1a635a3c75678223e3008da62018e60a50f9ed954083</citedby><cites>FETCH-LOGICAL-c306t-92e3067f49ecb06563c4f1a635a3c75678223e3008da62018e60a50f9ed954083</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/317862$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,792,23910,23911,25119,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/317862$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=3302895$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Giannakis, G.B.</creatorcontrib><creatorcontrib>Guotong Zhou</creatorcontrib><title>Parameter estimation of cyclostationary AM time series with application to missing observations</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>Time series with systematic misses occur often in practice and can be modeled as amplitude modulated ARMA processes. With this as a motivating application, modeling of cyclostationary amplitude modulated time series is addressed in the paper. Assuming that the modulating sequence is (almost) periodic, parameter estimation algorithms are developed based on second- and higher order cumulants of the resulting cyclostationary observations, which may be corrupted by any additive stationary noise of unknown covariance. If unknown, the modulating sequence can be recovered even in the presence of additive (perhaps nonstationary and colored) Gaussian, or any symmetrically distributed, noise. If the ARMA process is nonGaussian, cyclic cumulants of order greater than three can identify (non)causal and (non)minimum phase models from partial noisy data. Simulation experiments corroborate the theoretical results.< ></description><subject>Additive noise</subject><subject>Amplitude modulation</subject><subject>Applied sciences</subject><subject>Colored noise</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>Gaussian noise</subject><subject>Information, signal and communications theory</subject><subject>Parameter estimation</subject><subject>Phase noise</subject><subject>Radar applications</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Telecommunications and information theory</subject><subject>Time series analysis</subject><subject>Underwater acoustics</subject><subject>Underwater communication</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1994</creationdate><recordtype>article</recordtype><recordid>eNpFkMtLAzEQxoMoWKsHr55yEMHDarLZvI6l-IKKHhS8hTSd1ch2syZbpf-9abfoaV6_-ZhvEDql5IpSoq-lumJUKlHuoRHVFS1IJcV-zglnBVfy7RAdpfRJCK0qLUbIPNtol9BDxJB6v7S9Dy0ONXZr14TUb2sb13jyiPMYcILoIeEf339g23WNd8NKH_DSp-TbdxzmGfrettMxOqhtk-BkF8fo9fbmZXpfzJ7uHqaTWeEYEX2hS8hR1pUGNyeCC-aqmlrBuGVOciFVWbKMELWwoiRUgSCWk1rDQvOKKDZGF4NuF8PXKlsx-RoHTWNbCKtkSsV5NrwBLwfQxZBShNp0MduOa0OJ2bzQSGWGF2b2fCdqk7NNHW3rfPpbYIyUSvOMnQ2YB4D_6aDxC-FVeRk</recordid><startdate>19940901</startdate><enddate>19940901</enddate><creator>Giannakis, G.B.</creator><creator>Guotong Zhou</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19940901</creationdate><title>Parameter estimation of cyclostationary AM time series with application to missing observations</title><author>Giannakis, G.B. ; Guotong Zhou</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c306t-92e3067f49ecb06563c4f1a635a3c75678223e3008da62018e60a50f9ed954083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1994</creationdate><topic>Additive noise</topic><topic>Amplitude modulation</topic><topic>Applied sciences</topic><topic>Colored noise</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Exact sciences and technology</topic><topic>Gaussian noise</topic><topic>Information, signal and communications theory</topic><topic>Parameter estimation</topic><topic>Phase noise</topic><topic>Radar applications</topic><topic>Signal and communications theory</topic><topic>Signal, noise</topic><topic>Telecommunications and information theory</topic><topic>Time series analysis</topic><topic>Underwater acoustics</topic><topic>Underwater communication</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Giannakis, G.B.</creatorcontrib><creatorcontrib>Guotong Zhou</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Giannakis, G.B.</au><au>Guotong Zhou</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Parameter estimation of cyclostationary AM time series with application to missing observations</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>1994-09-01</date><risdate>1994</risdate><volume>42</volume><issue>9</issue><spage>2408</spage><epage>2419</epage><pages>2408-2419</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>Time series with systematic misses occur often in practice and can be modeled as amplitude modulated ARMA processes. With this as a motivating application, modeling of cyclostationary amplitude modulated time series is addressed in the paper. Assuming that the modulating sequence is (almost) periodic, parameter estimation algorithms are developed based on second- and higher order cumulants of the resulting cyclostationary observations, which may be corrupted by any additive stationary noise of unknown covariance. If unknown, the modulating sequence can be recovered even in the presence of additive (perhaps nonstationary and colored) Gaussian, or any symmetrically distributed, noise. If the ARMA process is nonGaussian, cyclic cumulants of order greater than three can identify (non)causal and (non)minimum phase models from partial noisy data. Simulation experiments corroborate the theoretical results.< ></abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/78.317862</doi><tpages>12</tpages></addata></record> |
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| ispartof | IEEE transactions on signal processing, 1994-09, Vol.42 (9), p.2408-2419 |
| issn | 1053-587X 1941-0476 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Additive noise Amplitude modulation Applied sciences Colored noise Detection, estimation, filtering, equalization, prediction Exact sciences and technology Gaussian noise Information, signal and communications theory Parameter estimation Phase noise Radar applications Signal and communications theory Signal, noise Telecommunications and information theory Time series analysis Underwater acoustics Underwater communication |
| title | Parameter estimation of cyclostationary AM time series with application to missing observations |
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