Enhanced Rapid Autofocus Back-Projection for PBSAR Based on the GEO Satellite

The passive bistatic synthetic aperture radar (PBSAR) is recognized as a critical developmental direction for future radar systems. To validate its operational feasibility, we designed a PBSAR system. However, significant measurement errors were observed to degrade imaging quality. Conventional auto...

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Main Authors: Te Zhao, Jun Wang, Zuhan Cheng, Ziqian Huang, Jiaqi Song
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Remote Sensing
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Online Access:https://www.mdpi.com/2072-4292/17/13/2239
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author Te Zhao
Jun Wang
Zuhan Cheng
Ziqian Huang
Jiaqi Song
author_facet Te Zhao
Jun Wang
Zuhan Cheng
Ziqian Huang
Jiaqi Song
author_sort Te Zhao
collection DOAJ
description The passive bistatic synthetic aperture radar (PBSAR) is recognized as a critical developmental direction for future radar systems. To validate its operational feasibility, we designed a PBSAR system. However, significant measurement errors were observed to degrade imaging quality. Conventional autofocusing algorithms operate under the assumption that measurement errors primarily perturb phase components while exerting negligible influence on signal envelopes. The results from the system demonstrate the invalidity of this assumption, and the performance of conventional autofocusing algorithms severely degrades under enhanced resolution requirements. To address this limitation, we propose a frequency-domain division-based multi-stage autofocusing framework. This approach improves the frequency-dependent characterization of phase errors and incorporates an image sharpness-optimized autofocusing strategy. The estimated phase errors are directly applied for signal-level compensation, yielding refocused imagery with enhanced clarity while achieving an efficiency improvement exceeding 75%. Furthermore, we introduce a ground Cartesian back projection algorithm to adapt it to the PBSAR architecture, significantly improving computational efficiency in autofocusing processing. The integration of the proposed autofocusing algorithm with the accelerated imaging framework achieves an enhancement in autofocusing performance and a computational efficiency improvement by an order of magnitude. Simulations and experimental validations confirm that the proposed methodology exhibits marked advantages in both operational efficiency and focusing performance.
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spelling doaj-art-dfe7078a4baf467a8b08c480167d9d3d2025-08-20T03:29:02ZengMDPI AGRemote Sensing2072-42922025-06-011713223910.3390/rs17132239Enhanced Rapid Autofocus Back-Projection for PBSAR Based on the GEO SatelliteTe Zhao0Jun Wang1Zuhan Cheng2Ziqian Huang3Jiaqi Song4National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaXi’an Research Institute of Navigation Technology, Xi’an 710068, ChinaThe passive bistatic synthetic aperture radar (PBSAR) is recognized as a critical developmental direction for future radar systems. To validate its operational feasibility, we designed a PBSAR system. However, significant measurement errors were observed to degrade imaging quality. Conventional autofocusing algorithms operate under the assumption that measurement errors primarily perturb phase components while exerting negligible influence on signal envelopes. The results from the system demonstrate the invalidity of this assumption, and the performance of conventional autofocusing algorithms severely degrades under enhanced resolution requirements. To address this limitation, we propose a frequency-domain division-based multi-stage autofocusing framework. This approach improves the frequency-dependent characterization of phase errors and incorporates an image sharpness-optimized autofocusing strategy. The estimated phase errors are directly applied for signal-level compensation, yielding refocused imagery with enhanced clarity while achieving an efficiency improvement exceeding 75%. Furthermore, we introduce a ground Cartesian back projection algorithm to adapt it to the PBSAR architecture, significantly improving computational efficiency in autofocusing processing. The integration of the proposed autofocusing algorithm with the accelerated imaging framework achieves an enhancement in autofocusing performance and a computational efficiency improvement by an order of magnitude. Simulations and experimental validations confirm that the proposed methodology exhibits marked advantages in both operational efficiency and focusing performance.https://www.mdpi.com/2072-4292/17/13/2239passive bistatic synthetic aperture radar (PBSAR)geostationary orbit (GEO) satelliteback projection (BP)autofocus (AF)motion error compensation (MEC)
spellingShingle Te Zhao
Jun Wang
Zuhan Cheng
Ziqian Huang
Jiaqi Song
Enhanced Rapid Autofocus Back-Projection for PBSAR Based on the GEO Satellite
Remote Sensing
passive bistatic synthetic aperture radar (PBSAR)
geostationary orbit (GEO) satellite
back projection (BP)
autofocus (AF)
motion error compensation (MEC)
title Enhanced Rapid Autofocus Back-Projection for PBSAR Based on the GEO Satellite
title_full Enhanced Rapid Autofocus Back-Projection for PBSAR Based on the GEO Satellite
title_fullStr Enhanced Rapid Autofocus Back-Projection for PBSAR Based on the GEO Satellite
title_full_unstemmed Enhanced Rapid Autofocus Back-Projection for PBSAR Based on the GEO Satellite
title_short Enhanced Rapid Autofocus Back-Projection for PBSAR Based on the GEO Satellite
title_sort enhanced rapid autofocus back projection for pbsar based on the geo satellite
topic passive bistatic synthetic aperture radar (PBSAR)
geostationary orbit (GEO) satellite
back projection (BP)
autofocus (AF)
motion error compensation (MEC)
url https://www.mdpi.com/2072-4292/17/13/2239
work_keys_str_mv AT tezhao enhancedrapidautofocusbackprojectionforpbsarbasedonthegeosatellite
AT junwang enhancedrapidautofocusbackprojectionforpbsarbasedonthegeosatellite
AT zuhancheng enhancedrapidautofocusbackprojectionforpbsarbasedonthegeosatellite
AT ziqianhuang enhancedrapidautofocusbackprojectionforpbsarbasedonthegeosatellite
AT jiaqisong enhancedrapidautofocusbackprojectionforpbsarbasedonthegeosatellite