Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning
The along-track resolution of conventional airborne synthetic aperture radar altimetry (SARAL), where the coherent processing interval (CPI) is the burst length, is not a full resolution due to beam limited. For a fully focused radar altimeter image, a novel airborne SARAL processing algorithm based...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10239313/ |
| _version_ | 1797682053998706688 |
|---|---|
| author | Cheng Fang Ming Sun Bo Huang Fangheng Guan Honghao Zhou Xianhua Liao Lei Yang |
| author_facet | Cheng Fang Ming Sun Bo Huang Fangheng Guan Honghao Zhou Xianhua Liao Lei Yang |
| author_sort | Cheng Fang |
| collection | DOAJ |
| description | The along-track resolution of conventional airborne synthetic aperture radar altimetry (SARAL), where the coherent processing interval (CPI) is the burst length, is not a full resolution due to beam limited. For a fully focused radar altimeter image, a novel airborne SARAL processing algorithm based on a long CPI is proposed in this article. The long CPI is capable of achieving higher azimuthal resolution than that in the conventional altimeter with limited number of pulses. To conquer the problem of atmosphere turbulence and in accordance of motion deviation of the airborne platform, a subaperture phase gradient autofocusing framework is introduced to alleviate nonsystematic phase errors (NsPE). In this framework, NsPE is estimated and compensated in along-track so that a fully focused delayed Doppler map (DDM) can be guaranteed. Finally, the height parameter estimation is performed to the 1-D altimeter echoes after multilooking processing of DDM to improve the estimation accuracy. Given that the conventional retracking algorithm is sensitive to noise, which may degrade the estimation accuracy, a flexible Bayesian method is designed in a hierarchical manner for the SARAL retracking. The SARAL raw data are utilized in the experiment. The results of image entropy and RMSE demonstrate the effectiveness and superiority of the proposed algorithm both qualitatively and quantitatively. |
| first_indexed | 2024-03-11T23:53:57Z |
| format | Article |
| id | doaj.art-475b0a9a3e764c93a801ebe11f7c2ea4 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-11T23:53:57Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-475b0a9a3e764c93a801ebe11f7c2ea42023-09-18T23:00:14ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01168135814810.1109/JSTARS.2023.331191310239313Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian LearningCheng Fang0https://orcid.org/0000-0002-2696-2721Ming Sun1https://orcid.org/0009-0002-6753-3568Bo Huang2https://orcid.org/0009-0006-6302-3260Fangheng Guan3https://orcid.org/0000-0003-3819-1087Honghao Zhou4https://orcid.org/0009-0001-9850-5749Xianhua Liao5https://orcid.org/0009-0003-6522-7839Lei Yang6https://orcid.org/0000-0002-3856-0914Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaTianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaInstitute of Electronics Engineering, China Academy of Engineering Physics, Mianyang, ChinaTianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaTianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaTianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaTianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaThe along-track resolution of conventional airborne synthetic aperture radar altimetry (SARAL), where the coherent processing interval (CPI) is the burst length, is not a full resolution due to beam limited. For a fully focused radar altimeter image, a novel airborne SARAL processing algorithm based on a long CPI is proposed in this article. The long CPI is capable of achieving higher azimuthal resolution than that in the conventional altimeter with limited number of pulses. To conquer the problem of atmosphere turbulence and in accordance of motion deviation of the airborne platform, a subaperture phase gradient autofocusing framework is introduced to alleviate nonsystematic phase errors (NsPE). In this framework, NsPE is estimated and compensated in along-track so that a fully focused delayed Doppler map (DDM) can be guaranteed. Finally, the height parameter estimation is performed to the 1-D altimeter echoes after multilooking processing of DDM to improve the estimation accuracy. Given that the conventional retracking algorithm is sensitive to noise, which may degrade the estimation accuracy, a flexible Bayesian method is designed in a hierarchical manner for the SARAL retracking. The SARAL raw data are utilized in the experiment. The results of image entropy and RMSE demonstrate the effectiveness and superiority of the proposed algorithm both qualitatively and quantitatively.https://ieeexplore.ieee.org/document/10239313/Coherent processing interval (CPI)hierarchical Bayesian learningphase gradient autofocusing (PGA)synthetic aperture radar altimeter (SARAL) |
| spellingShingle | Cheng Fang Ming Sun Bo Huang Fangheng Guan Honghao Zhou Xianhua Liao Lei Yang Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Coherent processing interval (CPI) hierarchical Bayesian learning phase gradient autofocusing (PGA) synthetic aperture radar altimeter (SARAL) |
| title | Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning |
| title_full | Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning |
| title_fullStr | Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning |
| title_full_unstemmed | Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning |
| title_short | Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning |
| title_sort | altimetric parameter estimation in long coherent processing by airborne delay doppler altimetry based on bayesian learning |
| topic | Coherent processing interval (CPI) hierarchical Bayesian learning phase gradient autofocusing (PGA) synthetic aperture radar altimeter (SARAL) |
| url | https://ieeexplore.ieee.org/document/10239313/ |
| work_keys_str_mv | AT chengfang altimetricparameterestimationinlongcoherentprocessingbyairbornedelaydoppleraltimetrybasedonbayesianlearning AT mingsun altimetricparameterestimationinlongcoherentprocessingbyairbornedelaydoppleraltimetrybasedonbayesianlearning AT bohuang altimetricparameterestimationinlongcoherentprocessingbyairbornedelaydoppleraltimetrybasedonbayesianlearning AT fanghengguan altimetricparameterestimationinlongcoherentprocessingbyairbornedelaydoppleraltimetrybasedonbayesianlearning AT honghaozhou altimetricparameterestimationinlongcoherentprocessingbyairbornedelaydoppleraltimetrybasedonbayesianlearning AT xianhualiao altimetricparameterestimationinlongcoherentprocessingbyairbornedelaydoppleraltimetrybasedonbayesianlearning AT leiyang altimetricparameterestimationinlongcoherentprocessingbyairbornedelaydoppleraltimetrybasedonbayesianlearning |