Sub-Nyquist SAR Based on Pseudo-Random Time-Space Modulation

Abstract

Sub-Nyquist sampling technology can ease the conflict between high resolution and wide swath in a synthetic aperture radar (SAR) system. However, the existing sub-Nyquist SAR imposes a constraint on the type of the observed scene and can only reconstruct the scene with small sparsity (i.e., number of significant coefficients). The information channel model of microwave imaging radar based on information theory, in which scene, echo, and the mapping relation between the two correspond to information source, sink, and channel, is built, and noisy-channel coding theorem explains the reason for the aforementioned under this model. To allow the wider application of sub-Nyquist SAR, this paper proposes sub-Nyquist SAR based on pseudo-random space-time modulation. This modulation is the spatial and temporal phase modulation to the traditional SAR raw data and can increase the mutual information of information source and sink so that the scenes with large sparsity can be reconstructed. Simulations of scenes with different sparsity, e.g., an ocean with several ships and urban scenes, were run to verify the validity of our proposed method, and the results show that the scenes with large sparsity can be successfully reconstructed.

Sub-Nyquist SAR Imaging And Error Correction Via Optimization-based Algorithm

“…These requirements are simultaneously satisfied by advanced observing modes, e.g., the azimuthal multi-channels SAR 3 and MIMO SAR 4 , and these observing modes both have the characteristics of large amounts of data and long antenna. As the CS theorem develops, a novel imaging mode named sub-Nyquist SAR based on pseudo-random space-time modulation has been proposed without large data and long antenna, and sub-Nyquist SAR imaging based on CS algorithms can recover the scene 8 .…”

“…Although the azimuthal multi-channel SAR 3 and multi-input multi-output (MIMO) SAR 4 can achieve high azimuthal resolution and wide range swath, it has a large amount of data and a large antenna. As the compressive sensing (CS) theorem develops, an innovative system concept called sub-Nyquist SAR based on pseudo-random time-space modulation breaks the limitation of the Nyquist sampling theorem with a single channel 8 . Simultaneously, the spatial and temporal phase modulation based on the optimization-based algorithm guarantees azimuthal resolution and wide-swath coverage mosaicked by several range subswaths.…”

“…Simultaneously, the spatial and temporal phase modulation based on the optimization-based algorithm guarantees azimuthal resolution and wide-swath coverage mosaicked by several range subswaths. It adopts sub-Nyquist sampling along the azimuthal dimension and the observed scene is recovered by sub-Nyquist SAR imaging 8 .…”

Sub-Nyquist SAR Imaging and Error Correction Via an Optimization-Based Algorithm

“…Although the azimuthal multi-channel SAR [3] and multi-input multi-output (MIMO) SAR [4] can achieve a high azimuthal resolution and wide-range swath, it has a large amount of data and a large antenna. As the compressive sensing (CS) theorem posits [5][6][7], an innovative system concept called sub-Nyquist SAR based on pseudo-random time-space modulation breaks the limitation of the Nyquist sampling theorem with a single channel [8]. Simultaneously, the spatial and temporal phase modulation based on the optimization-based algorithm guarantees azimuthal resolution and wide-swath coverage mosaicked by several range sub-swaths.…”

“…Simultaneously, the spatial and temporal phase modulation based on the optimization-based algorithm guarantees azimuthal resolution and wide-swath coverage mosaicked by several range sub-swaths. It adopts sub-Nyquist sampling along the azimuthal dimension, and the observed scene is recovered by sub-Nyquist SAR imaging [8].…”