Search Results (3)

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. Full article

As an evolutionary advancement to conventional synthetic aperture radar (SAR), passive bistatic SAR (PBSAR) utilizing geostationary orbit (GEO) satellite signals demonstrates significant potential for high-resolution imaging. However, PBSAR faces dual challenges in computational efficiency and phase error compensation. Traditional accelerated back-projection (BP) variants developed from monostatic SAR are incompatible with PBSAR’s geometry, and autofocus BP (AFBP) methods exhibit prohibitive computational costs and inadequate space-variant phase error handling. This study first develops a bistatic ground Cartesian back-projection (BGCBP) algorithm through subimage wavenumber spectrum correction, specifically adapted to GEO-satellite-based PBSAR. Compared to conventional BP, the BGCBP achieves an order-of-magnitude complexity reduction without resolution degradation. Building upon this foundation, we propose a subimage autofocus BGCBP (SIAF-BGCBP) methodology, synergistically integrating autofocus processing with BGCBP’s accelerated framework. SIAF-BGCBP reduces phase estimation’s complexity by 90% through subimage pixel density optimization while maintaining estimation accuracy. Further enhancement of SIAF-BGCBP via geometric inversion would enable the precise compensation of space-variant phase errors while remaining efficient. Simulations and real-environment experiments verify the effectiveness of the proposed methods. Full article

High-resolution wide-swath (HRWS) synthetic aperture radar (SAR) plays an important role in remote sensing observation. However, the motion errors caused by the carrier platform’s instability severely degrade the performance of the HRWS SAR imaging. Conventional motion errors compensation methods have two drawbacks, i.e., (1) ignoring the spatial variation of the phase errors of pixels along the range direction of the scene, which leads to lower compensation accuracy, and (2) performing compensation after echo reconstruction, which fails to consider the difference in motion errors between channels, resulting in poor imaging performance in the azimuth direction. In this paper, to overcome these two drawbacks, a high-precision motion errors compensation method based on sub-image reconstruction (SI-MEC) for high-precision HRWS SAR imaging is proposed. The proposed method consists of three steps. Firstly, the motion errors of the platform are estimated by maximizing the intensity of strong points in multiple regions. Secondly, combined with the multichannel geometry, the equivalent phase centers (EPCs) used for sub-images imaging are corrected and the sub-images imaging is performed before reconstruction. Thirdly, the reconstruction is performed by using the sub-images. The proposed method has two advantages, i.e., (1) compensating for the spatially varying phase errors in the range direction, by correcting EPCs, to improve the imaging quality, and (2) compensating for the motion errors of each channel in sub-image imaging before reconstruction, to enhance the imaging quality in the azimuth direction. Moreover, the experimental results are provided to demonstrate that the proposed method outperforms PGA and BP-FMSA. Full article