Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework

This work focuses on 3D Radar imaging inverse problems. Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well. For example, biased scattering energy may be acceptable for screen imaging but not for scattering diagnosis. To address this issue, we propose a new task-oriented imaging framework. The imaging principle is task-oriented through an analysis phase to obtain task's demands. The imaging model is multi-cognition regularized to embed and fulfill demands. The imaging method is designed to be general-ized, where couplings between cognitions are decoupled and solved individually with approximation and variable-splitting techniques. Tasks include scattering diagnosis, person screen imaging, and parcel screening imaging are given as examples. Experiments on data from two systems indicate that the pro-posed framework outperforms the current ones in task-depended information retrieval

fabricationofrodshapedfeoohtherolesofpolyethyleneglycolandchlorineanion

β-FeOOH nanorods of 40 nm wide and 450 nm long were fabricated through precisely regulating the hydrolysis kinetics of Fe~(3+) in polyethylene glycol and the concentration of Cl- as the structure-directing agent. Detailed structural and chemical analyses of the intermediates during the synthesis identified that the strong interaction between PEG and Fe~(3+) modulated the hydrolysis kinetics of Fe~(3+) and prevented the aggregation of β-FeOOH nanorods; while Cl- provided sufficient nucleation sites, stabilized the hollow channel of β-FeOOH, and more importantly induced the growth of the nanorods along 001 direction

Fabrication of rod-shaped beta-FeOOH: the roles of polyethylene glycol and chlorine anion

beta-FeOOH nanorods of 40 nm wide and 450 nm long were fabricated through precisely regulating the hydrolysis kinetics of Fe3+ in polyethylene glycol and the concentration of Cl- as the structure-directing agent. Detailed structural and chemical analyses of the intermediates during the synthesis identified that the strong interaction between PEG and Fe3+ modulated the hydrolysis kinetics of Fe3+ and prevented the aggregation of beta-FeOOH nanorods; while Cl- provided sufficient nucleation sites, stabilized the hollow channel of beta-FeOOH, and more importantly induced the growth of the nanorods along [001] direction

fabricationofrodshapedfeoohtherolesofpolyethyleneglycolandchlorineanion

β-FeOOH nanorods of 40 nm wide and 450 nm long were fabricated through precisely regulating the hydrolysis kinetics of Fe~(3+) in polyethylene glycol and the concentration of Cl- as the structure-directing agent. Detailed structural and chemical analyses of the intermediates during the synthesis identified that the strong interaction between PEG and Fe~(3+) modulated the hydrolysis kinetics of Fe~(3+) and prevented the aggregation of β-FeOOH nanorods; while Cl- provided sufficient nucleation sites, stabilized the hollow channel of β-FeOOH, and more importantly induced the growth of the nanorods along 001 direction

CTV-Net: Complex-Valued TV-Driven Network With Nested Topology for 3-D SAR Imaging

10.1109/TNNLS.2022.3208252IEEE Transactions on Neural Networks and Learning Systems1-1

CIST: An Improved ISAR Imaging Method Using Convolution Neural Network

Compressive sensing (CS) has been widely utilized in inverse synthetic aperture radar (ISAR) imaging, since ISAR measured data are generally non-completed in cross-range direction, and CS-based imaging methods can obtain high-quality imaging results using under-sampled data. However, the traditional CS-based methods need to pre-define parameters and sparse transforms, which are tough to be hand-crafted. Besides, these methods usually require heavy computational cost with large matrices operation. In this paper, inspired by the adaptive parameter learning and rapidly reconstruction of convolution neural network (CNN), a novel imaging method, called convolution iterative shrinkage-thresholding (CIST) network, is proposed for ISAR efficient sparse imaging. CIST is capable of learning optimal parameters and sparse transforms throughout the CNN training process, instead of being manually defined. Specifically, CIST replaces the linear sparse transform with non-linear convolution operations. This new transform and essential parameters are learnable end-to-end across the iterations, which increases the flexibility and robustness of CIST. When compared with the traditional state-of-the-art CS imaging methods, both simulation and experimental results demonstrate that the proposed CIST-based ISAR imaging method can obtain imaging results of high quality, while maintaining high computational efficiency. CIST-based ISAR imaging is tens of times faster than other methods

FDBP-InSAR: An Efficient Algorithm for InSAR Imaging via Frequency Domain Back Projection

High-quality focusing with accurate phase-preserving is a significant and challenging step in interferometric synthetic aperture radar (InSAR) imaging. Compared with conventional frequency-based imaging algorithms, the time-domain back-projection algorithm (TDBPA) can greatly ensure the accuracy of imaging and phase-preserving by point-to-point coherent integration but suffers from huge computational complexity. In this paper, we propose an efficient InSAR imaging method, called a frequency-domain back-projection algorithm (FDBPA), to achieve high-resolution focusing and accurate phase-preserving of InSAR imaging. More specifically, FDBPA is utilized to replace the traditional point-to-point coherent integration of TDBPA with frequency-domain transform. It divides the echo spectrum into uniform grids and transforms the range compression data into the range frequency domain. Phase compensation and non-uniform Fourier transform of the underlying scene are implemented to achieve image focusing in the wavenumber domain. Then, the interferometric phase of the target scene can be preserved by accurate phase compensation of the target’s distance. FDBPA avoids the repetitive calculation of index values and point-to-point coherent integration which reduces the time complexity compared with TDBPA. The characteristics of focusing and phase-preserving of our method are analyzed via simulations and experiments. The results demonstrate the efficiency and high-quality imaging of the FDBPA method. It can improve the imaging efficiency by more than three times, while keeping similar imaging accuracy compared with TDBPA

3D SAR Imaging Method Based on Learned Sparse Prior

The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency. Sparse SAR imaging has grown in importance as a research branch in SAR imaging due to the high potential of sparse signal processing techniques based on Compressed Sensing (CS) to show high potential in reducing system complexity and improving imaging quality. However, traditional sparse imaging methods are still constrained by high computational complexity, nontrivial parameter tuning, and poor adaptability to weakly sparse scenes. To address these issues, we propose a new 3D SAR imaging method based on learned sparse priors inspired by the deep unfolding concept. First, the limitations of the matrix-vector linear representation model are discussed, and an imaging operator is introduced to improve the algorithm’s imaging efficiency. Furthermore, this research focuses on algorithm network details, such as network topology design, the problem of complex-valued propagations, optimization constraints of algorithm parameters, and network training details. Finally, through simulations and measured experiments, it is proved that the proposed method can improve the imaging accuracy while reducing the running time by more than one order of magnitude compared with the conventional sparse imaging algorithms

3-D SAR Autofocusing With Learned Sparsity

10.1109/TGRS.2022.3210547IEEE Transactions on Geoscience and Remote Sensing6