Loading…

High Precision Piston Error Sensing of Segmented Telescope Based on Frequency Domain Filtering

Piston error is the main component of the co-phase errors of segmented telescopes. In this paper, we innovatively performed frequency domain filtering and processing on the focal plane image of the segmented telescopes with mask added, and obtained the image that only reflects the piston error betwe...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE photonics journal 2024-12, Vol.16 (6), p.1-6
Main Authors:	Li, Dequan, Wang, Dong
Format:	Article
Language:	English
Subjects:	Adaptive optics Feature extraction Filtering frequency domain filtering Frequency-domain analysis Mirrors Optical diffraction Optical filters Optical imaging Optical reflection piston sensing Pistons Segmented telescope
Citations:	Items that this one cites
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

cited_by
cites	cdi_FETCH-LOGICAL-c1309-b88d632555c9741d6e46d8f24fa932b96953acf1eb36e60eeba3d4a640ec24513
container_end_page	6
container_issue	6
container_start_page	1
container_title	IEEE photonics journal
container_volume	16
creator	Li, Dequan Wang, Dong
description	Piston error is the main component of the co-phase errors of segmented telescopes. In this paper, we innovatively performed frequency domain filtering and processing on the focal plane image of the segmented telescopes with mask added, and obtained the image that only reflects the piston error between each submirror and the reference submirror. The representation of feature image that reflects each submirror's piston error which obtained by this method is the same.Therefore, regardless of the number or the arrangement of submirrors, the single shallow convolutional neural network trained by any of the extracted submirror interference image dataset can be used to achieve high-precision detection of different submirror piston errors.Finally, simulation experiment results show the effectiveness of the proposed method.
doi_str_mv	10.1109/JPHOT.2024.3497182
format	article
fullrecord	<record><control><sourceid>doaj_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_JPHOT_2024_3497182</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10752339</ieee_id><doaj_id>oai_doaj_org_article_7a8fafa8e5484cd2a33a74b68c501e3a</doaj_id><sourcerecordid>oai_doaj_org_article_7a8fafa8e5484cd2a33a74b68c501e3a</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1309-b88d632555c9741d6e46d8f24fa932b96953acf1eb36e60eeba3d4a640ec24513</originalsourceid><addsrcrecordid>eNpNkM1OwkAUhRujiYi-gHHRFwDnv52lIgiGBBJx6-R2eluHlA7O4IK3t_zEuDrnntzzLU6S3FMypJTox7fldLEaMsLEkAud0ZxdJD2qBR8QJbLLPy_ldXIT45oQpanUveRz6uqvdBnQuuh8my5d3HUyDsGH9B3b6No69VVn6w22OyzTFTYYrd9i-gyxu7vvScDvH2ztPn3xG3Bd4Jodhq56m1xV0ES8O2s_-ZiMV6PpYL54nY2e5gNLOdGDIs9LxZmU0upM0FKhUGVeMVGB5qzQSksOtqJYcIWKIBbASwFKELRMSMr7yezELT2szTa4DYS98eDMMfChNhB2zjZoMsgrqCBHKXJhSwacQyYKlVtJKHLoWOzEssHHGLD641FiDmub49rmsLY5r92VHk4lh4j_CplknGv-C2CAfEQ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>High Precision Piston Error Sensing of Segmented Telescope Based on Frequency Domain Filtering</title><source>IEEE Open Access Journals</source><creator>Li, Dequan ; Wang, Dong</creator><creatorcontrib>Li, Dequan ; Wang, Dong</creatorcontrib><description>Piston error is the main component of the co-phase errors of segmented telescopes. In this paper, we innovatively performed frequency domain filtering and processing on the focal plane image of the segmented telescopes with mask added, and obtained the image that only reflects the piston error between each submirror and the reference submirror. The representation of feature image that reflects each submirror's piston error which obtained by this method is the same.Therefore, regardless of the number or the arrangement of submirrors, the single shallow convolutional neural network trained by any of the extracted submirror interference image dataset can be used to achieve high-precision detection of different submirror piston errors.Finally, simulation experiment results show the effectiveness of the proposed method.</description><identifier>ISSN: 1943-0655</identifier><identifier>EISSN: 1943-0647</identifier><identifier>DOI: 10.1109/JPHOT.2024.3497182</identifier><identifier>CODEN: PJHOC3</identifier><language>eng</language><publisher>IEEE</publisher><subject>Adaptive optics ; Feature extraction ; Filtering ; frequency domain filtering ; Frequency-domain analysis ; Mirrors ; Optical diffraction ; Optical filters ; Optical imaging ; Optical reflection ; piston sensing ; Pistons ; Segmented telescope</subject><ispartof>IEEE photonics journal, 2024-12, Vol.16 (6), p.1-6</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1309-b88d632555c9741d6e46d8f24fa932b96953acf1eb36e60eeba3d4a640ec24513</cites><orcidid>0000-0003-0229-5159</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10752339$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,27633,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Li, Dequan</creatorcontrib><creatorcontrib>Wang, Dong</creatorcontrib><title>High Precision Piston Error Sensing of Segmented Telescope Based on Frequency Domain Filtering</title><title>IEEE photonics journal</title><addtitle>JPHOT</addtitle><description>Piston error is the main component of the co-phase errors of segmented telescopes. In this paper, we innovatively performed frequency domain filtering and processing on the focal plane image of the segmented telescopes with mask added, and obtained the image that only reflects the piston error between each submirror and the reference submirror. The representation of feature image that reflects each submirror's piston error which obtained by this method is the same.Therefore, regardless of the number or the arrangement of submirrors, the single shallow convolutional neural network trained by any of the extracted submirror interference image dataset can be used to achieve high-precision detection of different submirror piston errors.Finally, simulation experiment results show the effectiveness of the proposed method.</description><subject>Adaptive optics</subject><subject>Feature extraction</subject><subject>Filtering</subject><subject>frequency domain filtering</subject><subject>Frequency-domain analysis</subject><subject>Mirrors</subject><subject>Optical diffraction</subject><subject>Optical filters</subject><subject>Optical imaging</subject><subject>Optical reflection</subject><subject>piston sensing</subject><subject>Pistons</subject><subject>Segmented telescope</subject><issn>1943-0655</issn><issn>1943-0647</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>DOA</sourceid><recordid>eNpNkM1OwkAUhRujiYi-gHHRFwDnv52lIgiGBBJx6-R2eluHlA7O4IK3t_zEuDrnntzzLU6S3FMypJTox7fldLEaMsLEkAud0ZxdJD2qBR8QJbLLPy_ldXIT45oQpanUveRz6uqvdBnQuuh8my5d3HUyDsGH9B3b6No69VVn6w22OyzTFTYYrd9i-gyxu7vvScDvH2ztPn3xG3Bd4Jodhq56m1xV0ES8O2s_-ZiMV6PpYL54nY2e5gNLOdGDIs9LxZmU0upM0FKhUGVeMVGB5qzQSksOtqJYcIWKIBbASwFKELRMSMr7yezELT2szTa4DYS98eDMMfChNhB2zjZoMsgrqCBHKXJhSwacQyYKlVtJKHLoWOzEssHHGLD641FiDmub49rmsLY5r92VHk4lh4j_CplknGv-C2CAfEQ</recordid><startdate>202412</startdate><enddate>202412</enddate><creator>Li, Dequan</creator><creator>Wang, Dong</creator><general>IEEE</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-0229-5159</orcidid></search><sort><creationdate>202412</creationdate><title>High Precision Piston Error Sensing of Segmented Telescope Based on Frequency Domain Filtering</title><author>Li, Dequan ; Wang, Dong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1309-b88d632555c9741d6e46d8f24fa932b96953acf1eb36e60eeba3d4a640ec24513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adaptive optics</topic><topic>Feature extraction</topic><topic>Filtering</topic><topic>frequency domain filtering</topic><topic>Frequency-domain analysis</topic><topic>Mirrors</topic><topic>Optical diffraction</topic><topic>Optical filters</topic><topic>Optical imaging</topic><topic>Optical reflection</topic><topic>piston sensing</topic><topic>Pistons</topic><topic>Segmented telescope</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Dequan</creatorcontrib><creatorcontrib>Wang, Dong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE photonics journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Dequan</au><au>Wang, Dong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>High Precision Piston Error Sensing of Segmented Telescope Based on Frequency Domain Filtering</atitle><jtitle>IEEE photonics journal</jtitle><stitle>JPHOT</stitle><date>2024-12</date><risdate>2024</risdate><volume>16</volume><issue>6</issue><spage>1</spage><epage>6</epage><pages>1-6</pages><issn>1943-0655</issn><eissn>1943-0647</eissn><coden>PJHOC3</coden><abstract>Piston error is the main component of the co-phase errors of segmented telescopes. In this paper, we innovatively performed frequency domain filtering and processing on the focal plane image of the segmented telescopes with mask added, and obtained the image that only reflects the piston error between each submirror and the reference submirror. The representation of feature image that reflects each submirror's piston error which obtained by this method is the same.Therefore, regardless of the number or the arrangement of submirrors, the single shallow convolutional neural network trained by any of the extracted submirror interference image dataset can be used to achieve high-precision detection of different submirror piston errors.Finally, simulation experiment results show the effectiveness of the proposed method.</abstract><pub>IEEE</pub><doi>10.1109/JPHOT.2024.3497182</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0003-0229-5159</orcidid><oa>free_for_read</oa></addata></record>
fulltext	fulltext
identifier	ISSN: 1943-0655
ispartof	IEEE photonics journal, 2024-12, Vol.16 (6), p.1-6
issn	1943-0655 1943-0647
language	eng
recordid	cdi_crossref_primary_10_1109_JPHOT_2024_3497182
source	IEEE Open Access Journals
subjects	Adaptive optics Feature extraction Filtering frequency domain filtering Frequency-domain analysis Mirrors Optical diffraction Optical filters Optical imaging Optical reflection piston sensing Pistons Segmented telescope
title	High Precision Piston Error Sensing of Segmented Telescope Based on Frequency Domain Filtering
url	http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T08%3A40%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-doaj_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=High%20Precision%20Piston%20Error%20Sensing%20of%20Segmented%20Telescope%20Based%20on%20Frequency%20Domain%20Filtering&rft.jtitle=IEEE%20photonics%20journal&rft.au=Li,%20Dequan&rft.date=2024-12&rft.volume=16&rft.issue=6&rft.spage=1&rft.epage=6&rft.pages=1-6&rft.issn=1943-0655&rft.eissn=1943-0647&rft.coden=PJHOC3&rft_id=info:doi/10.1109/JPHOT.2024.3497182&rft_dat=%3Cdoaj_cross%3Eoai_doaj_org_article_7a8fafa8e5484cd2a33a74b68c501e3a%3C/doaj_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c1309-b88d632555c9741d6e46d8f24fa932b96953acf1eb36e60eeba3d4a640ec24513%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10752339&rfr_iscdi=true