Motion Parameter Estimation in the SAR System With Low PRF Sampling
A novel approach to motion parameter estimation with low pulse repetition frequency (PRF) sampling based on compressed sensing (CS) theory is introduced. As is known to us, when PRF is less than the Doppler spectrum bandwidth, moving targets suffer both Doppler centroid frequency ambiguity and Doppl...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2010-07, Vol.7 (3), p.450-454 |
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| creator | Wu, Qisong Xing, Mengdao Qiu, Chengwei Liu, Baochang Bao, Zheng Yeo, Tat-Soon |
| description | A novel approach to motion parameter estimation with low pulse repetition frequency (PRF) sampling based on compressed sensing (CS) theory is introduced. As is known to us, when PRF is less than the Doppler spectrum bandwidth, moving targets suffer both Doppler centroid frequency ambiguity and Doppler spectrum ambiguity. Under this condition, the traditional parameter estimation method in the Doppler domain is out of action. The key of this letter converts motion parameter estimation in the synthetic aperture radar system with low PRF sampling into solving an optimization equation based on CS theory. Because moving targets in the scene can be regarded as sparse signals after clutter cancellation, an optimization algorithm based on CS theory is proposed to reconstruct sparse signals and meanwhile estimate the along-track velocities and azimuth positions of moving targets. Considering the fact that range cell migration of moving targets is not subject to PRF limitations, Radon transform is adopted to obtain unambiguous across-track velocities and range positions. Results on simulation and real data are provided to show the effectiveness of this method. |
| doi_str_mv | 10.1109/LGRS.2009.2039113 |
| format | Article |
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As is known to us, when PRF is less than the Doppler spectrum bandwidth, moving targets suffer both Doppler centroid frequency ambiguity and Doppler spectrum ambiguity. Under this condition, the traditional parameter estimation method in the Doppler domain is out of action. The key of this letter converts motion parameter estimation in the synthetic aperture radar system with low PRF sampling into solving an optimization equation based on CS theory. Because moving targets in the scene can be regarded as sparse signals after clutter cancellation, an optimization algorithm based on CS theory is proposed to reconstruct sparse signals and meanwhile estimate the along-track velocities and azimuth positions of moving targets. Considering the fact that range cell migration of moving targets is not subject to PRF limitations, Radon transform is adopted to obtain unambiguous across-track velocities and range positions. 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(IEEE) Jul 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-358be3307a77a5244d433cfefa2345fa0fdf8b7749a9bb64f6fe9d8022558f593</citedby><cites>FETCH-LOGICAL-c325t-358be3307a77a5244d433cfefa2345fa0fdf8b7749a9bb64f6fe9d8022558f593</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5406073$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5406073$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wu, Qisong</creatorcontrib><creatorcontrib>Xing, Mengdao</creatorcontrib><creatorcontrib>Qiu, Chengwei</creatorcontrib><creatorcontrib>Liu, Baochang</creatorcontrib><creatorcontrib>Bao, Zheng</creatorcontrib><creatorcontrib>Yeo, Tat-Soon</creatorcontrib><title>Motion Parameter Estimation in the SAR System With Low PRF Sampling</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>A novel approach to motion parameter estimation with low pulse repetition frequency (PRF) sampling based on compressed sensing (CS) theory is introduced. As is known to us, when PRF is less than the Doppler spectrum bandwidth, moving targets suffer both Doppler centroid frequency ambiguity and Doppler spectrum ambiguity. Under this condition, the traditional parameter estimation method in the Doppler domain is out of action. The key of this letter converts motion parameter estimation in the synthetic aperture radar system with low PRF sampling into solving an optimization equation based on CS theory. Because moving targets in the scene can be regarded as sparse signals after clutter cancellation, an optimization algorithm based on CS theory is proposed to reconstruct sparse signals and meanwhile estimate the along-track velocities and azimuth positions of moving targets. Considering the fact that range cell migration of moving targets is not subject to PRF limitations, Radon transform is adopted to obtain unambiguous across-track velocities and range positions. Results on simulation and real data are provided to show the effectiveness of this method.</description><subject>Ambiguity</subject><subject>Azimuth</subject><subject>Bandwidth</subject><subject>Clutter</subject><subject>Compressed sensing</subject><subject>Compressed sensing (CS)</subject><subject>Doppler</subject><subject>Doppler effect</subject><subject>Equations</subject><subject>Frequency estimation</subject><subject>Layout</subject><subject>Mathematical analysis</subject><subject>motion parameter estimation</subject><subject>Moving targets</subject><subject>Parameter estimation</subject><subject>Pulse compression methods</subject><subject>Radon transform (RT)</subject><subject>Sampling</subject><subject>Sampling methods</subject><subject>Studies</subject><subject>Synthetic aperture radar</subject><subject>synthetic aperture radar (SAR)</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1LAzEQQIMoWFd_gHgJePC0NZ9NcizFVmHF0lX0FrLbxG7p7tZNivTfm7XFg5eZYXgzzDwArjEaYozUfTZb5EOCkIqBKozpCRhgzmWKuMCnfc14ypX8OAcX3q8RIkxKMQCT5zZUbQPnpjO1DbaDDz5UtfltVg0MKwvz8QLmex9sDd-rsIJZ-w3niynMTb3dVM3nJThzZuPt1TEn4G368Dp5TLOX2dNknKUlJTyklMvCUoqEEcJwwtiSUVo66wyhjDuD3NLJQgimjCqKEXMjZ9VSIkLiH44rmoC7w95t137trA-6rnxpNxvT2HbntcRSUoKigATc_iPX7a5r4nEaIyIIpTSiCcAHquxa7zvr9LaLr3f7COnequ6t6t6qPlqNMzeHmcpa-8dzhkZIUPoDCRVxKw</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Wu, Qisong</creator><creator>Xing, Mengdao</creator><creator>Qiu, Chengwei</creator><creator>Liu, Baochang</creator><creator>Bao, Zheng</creator><creator>Yeo, Tat-Soon</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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As is known to us, when PRF is less than the Doppler spectrum bandwidth, moving targets suffer both Doppler centroid frequency ambiguity and Doppler spectrum ambiguity. Under this condition, the traditional parameter estimation method in the Doppler domain is out of action. The key of this letter converts motion parameter estimation in the synthetic aperture radar system with low PRF sampling into solving an optimization equation based on CS theory. Because moving targets in the scene can be regarded as sparse signals after clutter cancellation, an optimization algorithm based on CS theory is proposed to reconstruct sparse signals and meanwhile estimate the along-track velocities and azimuth positions of moving targets. Considering the fact that range cell migration of moving targets is not subject to PRF limitations, Radon transform is adopted to obtain unambiguous across-track velocities and range positions. 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| subjects | Ambiguity Azimuth Bandwidth Clutter Compressed sensing Compressed sensing (CS) Doppler Doppler effect Equations Frequency estimation Layout Mathematical analysis motion parameter estimation Moving targets Parameter estimation Pulse compression methods Radon transform (RT) Sampling Sampling methods Studies Synthetic aperture radar synthetic aperture radar (SAR) |
| title | Motion Parameter Estimation in the SAR System With Low PRF Sampling |
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