High-Resolution Azimuth Missing Data SAR Imaging Based on Sparse Representation Autofocusing

Abstract

Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) methods on AMD echo to reconstruct complete echo, which can be abbreviated as the AMD Imaging Algorithm (AMDIA). However, the State-of-the-Art AMDIA (SOA-AMDIA) do not consider the influence of motion phase errors, resulting in an unacceptable estimation error of the complete echo reconstruction. Therefore, in order to enhance the practical applicability of AMDIA, this article proposes an improved AMDIA using Sparse Representation Autofocusing (SRA-AMDIA). The proposed SRA-AMDIA aims to accurately focus the imaging result, even in the Phase Error AMD (PE-AMD) echo case. Firstly, a Phase-Compensation Function (PCF) based on the phase history of the scene centroid is designed. When the PCF is multiplied with the PE-AMD echo in the range-frequency domain, a coarse-focused sparse representation signal can be obtained in the range-Doppler domain. However, due to the influence of unknown PE, the sparsity of this sparse representation signal is unsatisfying, breaking the sparse constraints requirement of the CS method. Therefore, we introduced a minimum entropy autofocusing algorithm to autofocus this sparse representation signal. Next, the estimated PE is compensated for this sparse representation signal, and a more sparse representation signal is obtained. Hence, the non-PE complete echo can be reconstructed. Finally, the estimated complete echo can be used with classic imaging algorithms to obtain high-resolution imaging results under the PE-AMD condition. Simulation and real measured data have verified the effectiveness of the proposed SRA-AMDIA.

High Resolution Ranging with Small Sample Number under Low SNR Utilizing RIP-OMCS Strategy and AHRC l1 Minimization for Laser Radar

“…These formulas show that if a * is the solution of Equation ( 24), (a * , |a * |) is the solution of Equation ( 25); conversely, if (a * , u * ) is the solution of Equation ( 25), then a * is the solution of Equation (24).…”

“…To enhance the ranging performance of laser radar, two distinct methods have been proposed: (1) utilizing improved spectrum-estimation methods [14][15][16][17][18]; and (2) employing techniques like compressive sensing [19][20][21][22][23][24][25][26][27][28], multi-channel technology [29][30][31][32][33][34][35], or deep learning [36][37][38].…”

Bitemporal Remote Sensing Image Change Detection Network Based on Siamese-Attention Feedback Architecture

“…In our study, we introduce a Siamese-attention feedback architecture-based change detection network (SAFNet) for bitemporal remote sensing images, which has shown remarkable results on photo-level images. However, we also recognize that remote sensing images are often acquired through different means, such as synthetic aperture sonar (SAS) and synthetic aperture radar (SAR) [62,63]. The characteristics of these images, such as noise type, lighting conditions, etc., may differ from photorealistic images, which may have an impact on the performance of our method.…”