Motivated by the extraordinary modulation capability of the electromagnetic (EM) metasurface technique, a time-modulated metasurface-assisted moving target jamming method is proposed for the synthetic aperture radar (SAR) in this article. A time-modulated metasurface with real-time and flexible manipulation characteristics for the reflection phase of the EM wave under both normal and oblique incidences is designed, fabricated, and measured, respectively. It is capable of achieving composite phase modulation, which combines the advantages of the cosinusoidal-phase and subsection-phase modulations. Based on the designed metasurface, a spectral distribution with a series of discrete harmonic frequency components with varied frequency offsets and amplitudes can be formed. Furthermore, the composite phase modulation signal generated by the time-modulated metasurface is applied to interfere with the ground moving target indication (GMTI) for the SAR system, and the jamming effect of the metasurface on the moving target detection is analyzed in detail. The validity of the proposed jamming method is verified through theoretical analysis, metasurface modulation measurements, and SAR-GMTI experiments. Theoretical analysis and experimental results demonstrate that the adoption of the time-modulated metasurface modulation technique allows for the generation and detection of multiple discrete false moving targets with varying azimuth spacings and amplitudes for the victim SAR-GMTI system. The proposed method achieves effective moving target jamming and deception, expanding the application scenarios of metasurfaces in the field of radar electronic countermeasures.

Motivated by recent advances in deep learning, a novel deep complex-valued convolutional neural network (CV-CNN)-based method is proposed for ground moving target indication (GMTI) in a multichannel synthetic aperture radar (SAR) system. The proposed method integrates the SAR-GMTI task into a blind inverse problem solved by a deep CV-CNN named CV-GMTINet. To take advantage of the amplitude and phase information of complex multichannel SAR images, both feature maps and network parameters are extended into the complex domain. The proposed CV-GMTINet is designed by adopting complex-valued residual dense blocks (CV-RDBs) to adaptively learn complex hierarchical features. The trained CV-GMTINet, as a GMTI processor, can be applied to complex multichannel SAR images to discriminate moving targets from stationary clutter and refocus the moving target images simultaneously. Experiments on TerraSAR-X data show that the proposed method achieves significant improvements over existing state-of-the-art GMTI methods in both detection performance and refocusing accuracy, especially for the slow-moving target and the moving target with only along-track velocity.

Imaging of ground multiple moving targets in a synthetic aperture radar (SAR) system is a challenging task due to the fact that targets are defocused owing to motions and contaminated by the strong background clutter. Motivated by recent advances in deep learning, a novel deep convolutional neural network (CNN)-based method, DeepImaging, is proposed for ground moving target imaging (GMTIm). Different from conventional imaging methods relying on the prior knowledge of imaging, the proposed DeepImaging is directly trained to learn an implicit imaging model of multiple moving targets. It is free of motion parameter estimation and iteration process. Then, the trained DeepImaging, as an imaging processor, can be applied to the SAR complex received data after clutter suppression to achieve the multiple moving target imaging and the residual clutter elimination simultaneously. Simulations and experiments on the Gotcha data show that the proposed method achieves significant improvements over existing state-of-the-art GMTIm methods in terms of imaging quality and efficiency.

In this article, a new design approach for tunable filtering phase shifters on the basis of liquid crystal (LC) materials is reported. This proposed filtering LC phase shifter (LCPS) based on spoof surface plasmon polaritons (SSPP) simultaneously provides self-embedded fixed filtering performance and continuously tunable phase shifts. Equivalent circuit analysis and transmission parameter representations are implemented for the expounding of the fixed stopband caused by split-ring resonators (SRRs). Furthermore, due to the slow wave characteristics of SSPP waveguide, the higher tuning rate and figure of merit (FoM) can be intrinsically realized, which reveals that it can realize miniaturization and decrease insertion loss for specific phase shifts. In the end, the prototypes are designed, fabricated, and measured. The fabrication methodology of printed circuit board (PCB) technology ensures the mechanical stability, flexible design, perfect packaging, and low cost. The measurement results show the proposed bandstop filtering LCPS not only exhibited the fixed stopband from 7.61 to 8.18 GHz owning larger than 20-dB attenuation but also achieved the FoM more than 10.1°/dB from 6 to 7.45 GHz and more than 15.2°/dB from 8.37 to 10 GHz, which effectively verifies the theoretical analysis.

Synthetic aperture radar (SAR) is an advanced radar system capable of high-resolution imaging of targets under all-weather and all-day conditions. Therefore, effective jamming against SAR imaging systems is an important application scenario for achieving stealth and camouflage in modern information warfare. In this paper, a novel jamming imaging architecture based on the time-modulated metasurface is proposed capable of realizing two-dimensional (2D) jamming and camouflage for SAR imaging systems. Firstly, a dual-polarized and conformal time-modulated metasurface is designed, fabricated, and measured, respectively. A high degree of freedom in the manipulation of scattered waves is fully verified under the flat and conformal states of the designed tunable metasurface. On this basis, using this electromagnetic metasurface as the hardware foundation, a 2D multiple phase sectionalized modulation signal model is established and generated by the metasurface to create 2D and highly realistic false targets, achieving camouflage and jamming of protected targets against SAR imaging. The proposed method is thoroughly validated utilizing simulated SAR data and satellite RadarSat-2 data, demonstrating its efficiency, flexibility, and high deception capabilities. These findings establish a general paradigm for the application of time-modulated metasurfaces in novel information electronic warfare.

It is a challenging task to design a passive jamming method capable of both high fidelity and flexibility against synthetic aperture radar (SAR) imaging. Motivated by recent advances in the electromagnetic metasurface technique, a novel subsection-shift-Doppler-frequency jamming method based on the phase-tunable metasurface is proposed for the SAR system. Inspired by the realistic interference effects of the shift-frequency and segmented modulations, the proposed method flexibly imposes the subsection-shift-Doppler-frequency modulation on the SAR echo by the phase-tunable metasurface loaded with varactor diodes, which is designed to realize the continuous and real-time phase modulation through an external control system. Consequently, multiple false targets with flexible and controllable azimuth positions and verisimilar topological structures are generated instead of the protected target after receiving and processing the jamming signal by the victim SAR system. The influence of the phase-tunable metasurface modulation parameters on the jamming imaging effect is derived in theory. Simulations and experiments on satellite RadarSat-2 data are performed to verify the effectiveness of the proposed method. The theoretical analysis and experimental results show that the proposed method achieves the jamming imaging performance with high fidelity, high flexibility, low zero power, and low cost, effectively combining the advantages of traditional active and passive jamming.