Constrained Least Mean M-Estimation Adaptive Filtering Algorithm
In many applications, the constrained adaptive filtering algorithm has been widely studied. The classical constrained LMS algorithm is widely used because of its low computational complexity. However, the performance of constrained LMS algorithm will degrade under correlated input or non-Gaussian no...
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| Veröffentlicht in: | IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2021-04, Vol.68 (4), p.1507-1511 |
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| container_title | IEEE transactions on circuits and systems. II, Express briefs |
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| creator | Wang, Zhuonan Zhao, Haiquan Zeng, Xiangping |
| description | In many applications, the constrained adaptive filtering algorithm has been widely studied. The classical constrained LMS algorithm is widely used because of its low computational complexity. However, the performance of constrained LMS algorithm will degrade under correlated input or non-Gaussian noise. In order to overcome this defect, this brief proposes a constrained least mean M-estimation (CLMM) algorithm, which uses the M-estimation cost function for the constrained adaptive filter. Compared with the previous algorithms for non-Gaussian noise, such as constrained maximum correntropy criterion (CMCC) algorithm and constrained minimum error entropy (CMEE) algorithm, the proposed CLMM algorithm has lower computational complexity and better steady-state performance. In addition, the step-size range is determined by analyzing the mean square stability, which ensures the stability of the proposed CLMM algorithm. Simulation results illustrate that the proposed CLMM algorithm has better steady-state performance than previous algorithms in non-Gaussian noises with multi-peak distribution. |
| doi_str_mv | 10.1109/TCSII.2020.3022081 |
| format | Article |
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The classical constrained LMS algorithm is widely used because of its low computational complexity. However, the performance of constrained LMS algorithm will degrade under correlated input or non-Gaussian noise. In order to overcome this defect, this brief proposes a constrained least mean M-estimation (CLMM) algorithm, which uses the M-estimation cost function for the constrained adaptive filter. Compared with the previous algorithms for non-Gaussian noise, such as constrained maximum correntropy criterion (CMCC) algorithm and constrained minimum error entropy (CMEE) algorithm, the proposed CLMM algorithm has lower computational complexity and better steady-state performance. In addition, the step-size range is determined by analyzing the mean square stability, which ensures the stability of the proposed CLMM algorithm. Simulation results illustrate that the proposed CLMM algorithm has better steady-state performance than previous algorithms in non-Gaussian noises with multi-peak distribution.</description><identifier>ISSN: 1549-7747</identifier><identifier>EISSN: 1558-3791</identifier><identifier>DOI: 10.1109/TCSII.2020.3022081</identifier><identifier>CODEN: ICSPE5</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Adaptive algorithms ; Adaptive filters ; Adaptive systems ; Algorithms ; Circuit stability ; Complexity ; Computational complexity ; Constrained adaptive filtering ; Cost function ; Engineering ; Engineering, Electrical & Electronic ; Filtering algorithms ; M-estimate ; Mathematical model ; non-Gaussian noises ; Random noise ; Science & Technology ; Stability analysis ; Steady state ; system identification ; Technology</subject><ispartof>IEEE transactions on circuits and systems. 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(IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>29</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000634501300090</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c295t-f68a31618e655258dbb3ab906336d9f908ffc2d9720aa9a10b99b04f7cc0b9a3</citedby><cites>FETCH-LOGICAL-c295t-f68a31618e655258dbb3ab906336d9f908ffc2d9720aa9a10b99b04f7cc0b9a3</cites><orcidid>0000-0001-7246-5155 ; 0000-0001-8721-2548 ; 0000-0003-0198-1384</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9187213$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,39263,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9187213$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Zhuonan</creatorcontrib><creatorcontrib>Zhao, Haiquan</creatorcontrib><creatorcontrib>Zeng, Xiangping</creatorcontrib><title>Constrained Least Mean M-Estimation Adaptive Filtering Algorithm</title><title>IEEE transactions on circuits and systems. II, Express briefs</title><addtitle>TCSII</addtitle><addtitle>IEEE T CIRCUITS-II</addtitle><description>In many applications, the constrained adaptive filtering algorithm has been widely studied. The classical constrained LMS algorithm is widely used because of its low computational complexity. However, the performance of constrained LMS algorithm will degrade under correlated input or non-Gaussian noise. In order to overcome this defect, this brief proposes a constrained least mean M-estimation (CLMM) algorithm, which uses the M-estimation cost function for the constrained adaptive filter. Compared with the previous algorithms for non-Gaussian noise, such as constrained maximum correntropy criterion (CMCC) algorithm and constrained minimum error entropy (CMEE) algorithm, the proposed CLMM algorithm has lower computational complexity and better steady-state performance. In addition, the step-size range is determined by analyzing the mean square stability, which ensures the stability of the proposed CLMM algorithm. Simulation results illustrate that the proposed CLMM algorithm has better steady-state performance than previous algorithms in non-Gaussian noises with multi-peak distribution.</description><subject>Adaptive algorithms</subject><subject>Adaptive filters</subject><subject>Adaptive systems</subject><subject>Algorithms</subject><subject>Circuit stability</subject><subject>Complexity</subject><subject>Computational complexity</subject><subject>Constrained adaptive filtering</subject><subject>Cost function</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Filtering algorithms</subject><subject>M-estimate</subject><subject>Mathematical model</subject><subject>non-Gaussian noises</subject><subject>Random noise</subject><subject>Science & Technology</subject><subject>Stability analysis</subject><subject>Steady state</subject><subject>system identification</subject><subject>Technology</subject><issn>1549-7747</issn><issn>1558-3791</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>HGBXW</sourceid><recordid>eNqNkEFPwjAYhhujiYj-Ab0s8WiGX9t1a2-SBZQE4kHuTbd1WAIdtkXjv7cwoldPfQ_v8339HoRuMYwwBvG4LN9msxEBAiMKhADHZ2iAGeMpLQQ-P-RMpEWRFZfoyvs1ABFAyQA9lZ31wSljdZPMtfIhWWhlk0U68cFsVTCdTcaN2gXzqZOp2QTtjF0l482qcya8b6_RRas2Xt-c3iFaTifL8iWdvz7PyvE8rYlgIW1zrijOMdc5Y4TxpqqoqgTklOaNaAXwtq1JIwoCSgmFoRKigqwt6jpGRYfovh-7c93HXvsg193e2bhREgZ5DhkIElukb9Wu897pVu5cPMJ9SwzyIEoeRcmDKHkSFSHeQ1-66lpfG21r_QsCxD9mDDCNSUBpwtFJ2e1tiOjD_9HYvuvbRuu_lsC8IJjSH5UnhB8</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Wang, Zhuonan</creator><creator>Zhao, Haiquan</creator><creator>Zeng, Xiangping</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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II, Express briefs</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Zhuonan</au><au>Zhao, Haiquan</au><au>Zeng, Xiangping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Constrained Least Mean M-Estimation Adaptive Filtering Algorithm</atitle><jtitle>IEEE transactions on circuits and systems. II, Express briefs</jtitle><stitle>TCSII</stitle><stitle>IEEE T CIRCUITS-II</stitle><date>2021-04-01</date><risdate>2021</risdate><volume>68</volume><issue>4</issue><spage>1507</spage><epage>1511</epage><pages>1507-1511</pages><issn>1549-7747</issn><eissn>1558-3791</eissn><coden>ICSPE5</coden><abstract>In many applications, the constrained adaptive filtering algorithm has been widely studied. The classical constrained LMS algorithm is widely used because of its low computational complexity. However, the performance of constrained LMS algorithm will degrade under correlated input or non-Gaussian noise. In order to overcome this defect, this brief proposes a constrained least mean M-estimation (CLMM) algorithm, which uses the M-estimation cost function for the constrained adaptive filter. Compared with the previous algorithms for non-Gaussian noise, such as constrained maximum correntropy criterion (CMCC) algorithm and constrained minimum error entropy (CMEE) algorithm, the proposed CLMM algorithm has lower computational complexity and better steady-state performance. In addition, the step-size range is determined by analyzing the mean square stability, which ensures the stability of the proposed CLMM algorithm. Simulation results illustrate that the proposed CLMM algorithm has better steady-state performance than previous algorithms in non-Gaussian noises with multi-peak distribution.</abstract><cop>PISCATAWAY</cop><pub>IEEE</pub><doi>10.1109/TCSII.2020.3022081</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0001-7246-5155</orcidid><orcidid>https://orcid.org/0000-0001-8721-2548</orcidid><orcidid>https://orcid.org/0000-0003-0198-1384</orcidid></addata></record> |
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| subjects | Adaptive algorithms Adaptive filters Adaptive systems Algorithms Circuit stability Complexity Computational complexity Constrained adaptive filtering Cost function Engineering Engineering, Electrical & Electronic Filtering algorithms M-estimate Mathematical model non-Gaussian noises Random noise Science & Technology Stability analysis Steady state system identification Technology |
| title | Constrained Least Mean M-Estimation Adaptive Filtering Algorithm |
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