A Wave Peak Frequency Tracking Method Based on Two-Stage Recursive Extended Least Squares Identification Algorithm
This paper proposes the wave peak frequency tracking methods based on the least squares identification algorithm. The wave disturbance model is transformed into an autoregressive moving average (ARMA) model and a recursive extended least squares (RELS) algorithm is derived to identify the model para...
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| Veröffentlicht in: | IEEE access 2021, Vol.9, p.86514-86522 |
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| creator | Yuan, Jianping An, Shun Pan, Xinxiang Mao, Hongfei Wang, Longjin |
| description | This paper proposes the wave peak frequency tracking methods based on the least squares identification algorithm. The wave disturbance model is transformed into an autoregressive moving average (ARMA) model and a recursive extended least squares (RELS) algorithm is derived to identify the model parameters by using the auxiliary model identification idea. Furthermore, a two-stage recursive extended least squares (2S-RELS) algorithm is presented to improve the convergence speed by using the hierarchical identification principle. A ship heading control system with the wave peak frequency tracker is built to evaluate the effectiveness of the proposed algorithms. Finally, simulation results show that the proposed algorithms can estimate the wave peak frequency accurately and the 2S-RELS algorithm can improve the convergence speed effectively. |
| doi_str_mv | 10.1109/ACCESS.2021.3057454 |
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The wave disturbance model is transformed into an autoregressive moving average (ARMA) model and a recursive extended least squares (RELS) algorithm is derived to identify the model parameters by using the auxiliary model identification idea. Furthermore, a two-stage recursive extended least squares (2S-RELS) algorithm is presented to improve the convergence speed by using the hierarchical identification principle. A ship heading control system with the wave peak frequency tracker is built to evaluate the effectiveness of the proposed algorithms. Finally, simulation results show that the proposed algorithms can estimate the wave peak frequency accurately and the 2S-RELS algorithm can improve the convergence speed effectively.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2021.3057454</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Algorithms ; Autoregressive moving-average models ; Autoregressive processes ; Computational modeling ; Computer Science ; Computer Science, Information Systems ; Convergence ; Engineering ; Engineering, Electrical & Electronic ; Frequency control ; Frequency estimation ; Heuristic algorithms ; hierarchical identification ; Least squares ; Marine vehicles ; Parameter identification ; Peak frequency ; recursive identification ; Science & Technology ; Signal processing algorithms ; Technology ; Telecommunications ; Tracking ; Wave frequency tracker</subject><ispartof>IEEE access, 2021, Vol.9, p.86514-86522</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>3</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000673113900001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c408t-2bc4168817c82bb131baa6031c48b42ba0618389978250b420cc266bdf70230b3</citedby><cites>FETCH-LOGICAL-c408t-2bc4168817c82bb131baa6031c48b42ba0618389978250b420cc266bdf70230b3</cites><orcidid>0000-0003-1309-714X ; 0000-0003-0584-0554</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9349137$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,782,786,866,2104,2116,4026,27640,27930,27931,27932,39265,54940</link.rule.ids></links><search><creatorcontrib>Yuan, Jianping</creatorcontrib><creatorcontrib>An, Shun</creatorcontrib><creatorcontrib>Pan, Xinxiang</creatorcontrib><creatorcontrib>Mao, Hongfei</creatorcontrib><creatorcontrib>Wang, Longjin</creatorcontrib><title>A Wave Peak Frequency Tracking Method Based on Two-Stage Recursive Extended Least Squares Identification Algorithm</title><title>IEEE access</title><addtitle>Access</addtitle><addtitle>IEEE ACCESS</addtitle><description>This paper proposes the wave peak frequency tracking methods based on the least squares identification algorithm. The wave disturbance model is transformed into an autoregressive moving average (ARMA) model and a recursive extended least squares (RELS) algorithm is derived to identify the model parameters by using the auxiliary model identification idea. Furthermore, a two-stage recursive extended least squares (2S-RELS) algorithm is presented to improve the convergence speed by using the hierarchical identification principle. A ship heading control system with the wave peak frequency tracker is built to evaluate the effectiveness of the proposed algorithms. Finally, simulation results show that the proposed algorithms can estimate the wave peak frequency accurately and the 2S-RELS algorithm can improve the convergence speed effectively.</description><subject>Algorithms</subject><subject>Autoregressive moving-average models</subject><subject>Autoregressive processes</subject><subject>Computational modeling</subject><subject>Computer Science</subject><subject>Computer Science, Information Systems</subject><subject>Convergence</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Frequency control</subject><subject>Frequency estimation</subject><subject>Heuristic algorithms</subject><subject>hierarchical identification</subject><subject>Least squares</subject><subject>Marine vehicles</subject><subject>Parameter identification</subject><subject>Peak frequency</subject><subject>recursive identification</subject><subject>Science & Technology</subject><subject>Signal processing algorithms</subject><subject>Technology</subject><subject>Telecommunications</subject><subject>Tracking</subject><subject>Wave frequency tracker</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>HGBXW</sourceid><sourceid>DOA</sourceid><recordid>eNqNkU9vEzEQxVcIJKq2n6AXSxzRBv_bXe8xrNISKQhEgjhaY-9s6jRdt7ZD6bev060KR3yxNX6_N6N5RXHB6Iwx2n6ad91ivZ5xytlM0KqRlXxTnHBWt6WoRP32n_f74jzGHc1H5VLVnBRhTn7BbyTfEW7IZcD7A472kWwC2Bs3bslXTNe-J58hYk_8SDYPvlwn2CL5gfYQosvs4k_Csc__K4SYyPr-AAEjWfY4Jjc4C8llcr7f-uDS9e1Z8W6AfcTzl_u0-Hm52HRfytW3q2U3X5VWUpVKbqxktVKssYobwwQzADUVzEplJDdAa6aEattG8YrmCrWW17Xph4ZyQY04LZaTb-9hp--Cu4XwqD04_VzwYashJGf3qHsuVd1YMQyNlADWYJs7S9tmmwqAZa8Pk9dd8HlFMemdP4Qxj695JXN_LnmTVWJS2eBjDDi8dmVUH7PSU1b6mJV-ySpTaqIe0PghWpcTwFcyZ1U3gjHRHmNjnUvP6-z8YUwZ_fj_aFZfTGqH-FfVCtky0YgnR3KvSA</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Yuan, Jianping</creator><creator>An, Shun</creator><creator>Pan, Xinxiang</creator><creator>Mao, Hongfei</creator><creator>Wang, Longjin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The wave disturbance model is transformed into an autoregressive moving average (ARMA) model and a recursive extended least squares (RELS) algorithm is derived to identify the model parameters by using the auxiliary model identification idea. Furthermore, a two-stage recursive extended least squares (2S-RELS) algorithm is presented to improve the convergence speed by using the hierarchical identification principle. A ship heading control system with the wave peak frequency tracker is built to evaluate the effectiveness of the proposed algorithms. Finally, simulation results show that the proposed algorithms can estimate the wave peak frequency accurately and the 2S-RELS algorithm can improve the convergence speed effectively.</abstract><cop>PISCATAWAY</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2021.3057454</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-1309-714X</orcidid><orcidid>https://orcid.org/0000-0003-0584-0554</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Autoregressive moving-average models Autoregressive processes Computational modeling Computer Science Computer Science, Information Systems Convergence Engineering Engineering, Electrical & Electronic Frequency control Frequency estimation Heuristic algorithms hierarchical identification Least squares Marine vehicles Parameter identification Peak frequency recursive identification Science & Technology Signal processing algorithms Technology Telecommunications Tracking Wave frequency tracker |
| title | A Wave Peak Frequency Tracking Method Based on Two-Stage Recursive Extended Least Squares Identification Algorithm |
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