Super-Resolution Range and Velocity Estimations for SFA-OFDM Radar

Sparse frequency agile orthogonal frequency division multiplexing (SFA-OFDM) signal brings excellent performance to electronic counter-countermeasures (ECCM) and reduces the complexity of the radar system. However, frequency agility makes coherent processing a much more challenging task for the radar, which leads to the discontinuity of the echo phase in a coherent processing interval (CPI), so the fast Fourier transform (FFT)-based method is no longer a valid way to complete the coherent integration. To overcome this problem, we proposed a novel scheme to estimate both super-resolution range and velocity. The subcarriers of each pulse are firstly synthesized in time domain. Then, the range and velocity estimations for the SFA-OFDM radar are regarded as the parameter estimations of a linear array. Finally, both the super-resolution range and velocity are obtained by exploiting the multiple signal classification (MUSIC) algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Research on Efficient Reinforcement Learning for Adaptive Frequency-Agility Radar

Modern radar jamming scenarios are complex and changeable. In order to improve the adaptability of frequency-agile radar under complex environmental conditions, reinforcement learning (RL) is introduced into the radar anti-jamming research. There are two aspects of the radar system that do not obey with the Markov decision process (MDP), which is the basic theory of RL: Firstly, the radar cannot confirm the interference rules of the jammer in advance, resulting in unclear environmental boundaries; secondly, the radar has frequency-agility characteristics, which does not meet the sequence change requirements of the MDP. As the existing RL algorithm is directly applied to the radar system, there would be problems, such as low sample utilization rate, poor computational efficiency and large error oscillation amplitude. In this paper, an adaptive frequency agile radar anti-jamming efficient RL model is proposed. First, a radar-jammer system model based on Markov game (MG) established, and the Nash equilibrium point determined and set as a dynamic environment boundary. Subsequently, the state and behavioral structure of RL model is improved to be suitable for processing frequency-agile data. Experiments that our proposal effectively the anti-jamming performance and efficiency of frequency-agile radar.

Actively Controlled Frequency-Agile Fano-Resonant Metasurface for Broadband and Unity Modulation

The active control to the local resonant mode of metasurface is a promising route for improving the operation bandwidth limitation of metasurface. Here, we propose and experimentally demonstrated the active tunabilities in a frequency-agile Fano-resonant metasurface. The metasurface with a pair of asymmetric split ring resonators is integrated with double varactor diodes for active control of the sharp Fano resonance. It is found that the sharp Fano-type spectrum appears due to the near-field interferences between the collective electric and magnetic dipole modes. The physical insight is revealed through local field analysis, multipole decomposition and temporal coupled-mode theory. It is also found that the metasurface can be employed as a broadband and unity modulator. Hopefully, our results could inspire sophisticated electrically controlled photonic devices with novel functions.

Fano-Resonant Hybrid Metamaterial for Enhanced Nonlinear Tunability and Hysteresis Behavior

Artificial resonant metamaterial with subwavelength localized filed is promising for advanced nonlinear photonic applications. In this article, we demonstrate enhanced nonlinear frequency-agile response and hysteresis tunability in a Fano-resonant hybrid metamaterial. A ceramic cuboid is electromagnetically coupled with metal cut-wire structure to excite the high-Q Fano-resonant mode in the dielectric/metal hybrid metamaterial. It is found that the significant nonlinear response of the ceramic cuboid can be employed for realization of tunable metamaterials by exciting its magnetic mode, and the trapped mode with an asymmetric Fano-like resonance is beneficial to achieve notable nonlinear modulation on the scattering spectrum. The nonlinear tunability of both the ceramic structure and the ceramic/metal hybrid metamaterial is promising to extend the operation band of metamaterials, providing possibility in practical applications with enhanced light-matter interactions.

Robust Antijamming Strategy Design for Frequency-Agile Radar against Main Lobe Jamming

To combat main lobe jamming, preventive measures can be applied to radar in advance based on the concept of active antagonism, and efficient antijamming strategies can be designed through reinforcement learning. However, uncertainties in the radar and the jammer, which will result in a mismatch between the test and training environments, are not considered. Therefore, a robust antijamming strategy design method is proposed in this paper, in which frequency-agile radar and a main lobe jammer are considered. This problem is first formulated under the framework of Wasserstein robust reinforcement learning. Then, the method of imitation learning-based jamming strategy parameterization is presented to express the given jamming strategy mathematically. To reduce the number of parameters that require optimization, a perturbation method inspired by NoisyNet is also proposed. Finally, robust antijamming strategies are designed by incorporating jamming strategy parameterization and jamming strategy perturbation into Wasserstein robust reinforcement learning. The simulation results show that the robust antijamming strategy leads to improved radar performance compared with the nonrobust antijamming strategy when uncertainties exist in the radar and the jammer.