Jamming is a big threat to the survival of a radar system. Therefore, the recognition of radar jamming signal type is a part of radar countermeasure. Recently, convolutional neural networks (CNNs) have shown their effectiveness in radar signal processing, including jamming signal recognition. However, most of existing CNN methods do not regard radar jamming as a complex value signal. In this study, a complex-valued CNN (CV-CNN) is investigated to fully explore the inherent characteristics of a radar jamming signal, and we find that we can obtain better recognition accuracy using this method compared with a real-valued CNN (RV-CNN). CV-CNNs contain more parameters, which need more inference time. To reduce the parameter redundancy and speed up the recognition time, a fast CV-CNN (F-CV-CNN), which is based on pruning, is proposed for radar jamming signal fast recognition. The experimental results show that the CV-CNN and F-CV-CNN methods obtain good recognition performance in terms of accuracy and speed. The proposed methods open a new window for future research, which shows a huge potential of CV-CNN-based methods for radar signal processing.

In the complex battlefield electromagnetic environment, multiple jamming signals can enter the radar receiver simultaneously due to the development of jammers and modulation technology. The received compound jamming signals aggravate the difficulty of recognition and subsequent counter-countermeasure. In the face of strong overlapping signals and unseen jamming signal combinations, the performance of existing recognition methods usually seriously degrades. In this paper, an end-to-end multi-label classification framework combining a complex-valued convolutional neural network (CV-CNN) and jamming class representations is proposed to automatically recognize the jamming signal components of compound jamming signals. A basic multi-label CV-CNN (ML-CV-CNN) is first designed to directly process time–domain complex signals and fully retain jamming signal information. Then, the jamming class representations are generated using prototype clustering implemented by learning vector quantization, and they are fused with the ML-CV-CNN using class decoupling implemented by the attention mechanism to construct a multi-label class representation CV-CNN (ML-CR-CV-CNN), which can better learn the class-related features required for recognition. Finally, an adaptive threshold calibration is adopted to obtain optimal recognition results by multi-threshold discrimination. Simulation results verify that the proposed method has superior recognition performance, which is reflected in the strong robustness to the varying jamming-to-noise ratio (JNR) and power ratio, faster convergence speed with high JNRs, and better generalization for unseen jamming signal combinations.

This study simulated the broadening first‐order sea clutter spectrum and analysed the influence of ionosphere and bistatic angle on sea clutter spectrum for high‐frequency (HF) hybrid sky‐surface wave radar based on the mechanism of sea clutter broadening. Firstly, according to the propagation characteristics of HF waves in the ionosphere and the mechanism of the ionosphere contamination, a method of dynamic ionospheric phase contamination simulation is introduced. Secondly, the formula of the Bragg frequency of first‐order sea clutter is presented derived from the system configuration, and a simulation method of broadening sea clutter is given. The influence of bistatic angle and different ionosphere state on the first‐order sea clutter spectrum is further analysed. Finally, the results of theoretic analysis are examined with the experimental results based on the newly developed integrated HF sky‐surface wave radar experimental system, and the dominant factor of sea clutter broadening is studied. Simulation results show that the results of experiment consist with that of theoretic analysis, simulated broadening sea clutter can better describe the characteristics of measured sea clutter.

This paper considers the polyphase-coded FM (PCFM) waveform design with good spectral compatibility and correlation performance. In getting the desired PCFM waveform, we first design its sampled sequence with spectral and correlation considerations. To be specific, the power in prescribed stopbands is minimized to improve spectral compatibility, and correlation performance is enhanced by minimizing the integrated mainlobe energy (IME) and integrated sidelobe energy (ISE) of the autocorrelation function, which, respectively, control the range resolution and range sidelobe level. We deduce the optimal power spectrum density (PSD) condition for the considered problem and write the objective function as the residual error between the sequence's PSD and the optimal one. We also devise a way to employ fast Fourier transform (FFT) in calculating the gradient of the PSD residual error such that it can be efficiently minimized using a general gradient descent method. With the designed sequence, we propose a new sequence-to-waveform implementation to reconstruct the PCFM waveform. Unlike sequences designed in many existing studies, the PCFM waveform is much closer to the real emission and thus expected to suffer less distortion in physical implementation.

In high frequency (HF) hybrid sky‐surface wave radar, the first‐order sea clutter broadening is very severe under the influence of ionosphere and bistatic angle, which affects the detection of slow ship target. This study analyses and simulates the broadening first‐order sea clutter spectrum based on a modified first‐order ocean surface radar cross‐section (RCS) model using wide beam. The authors first introduce the working principle, range equation and positioning principle based on the newly‐developed integrated HF sky‐surface experimental system; and derive an expression for the first‐order Bragg frequency. Also presented is a modified RCS model of first‐order sea clutter, in which the HF sea clutter spectrum distribution characteristics and broadening mechanism are fully considered. Finally, the proposed broadening first‐order sea clutter spectrum model is examined by measured data. Simulation results indicate that the proposed first‐order sea clutter spectrum model can well describe the characteristics of measured sea clutter.

In the face of smart and varied jamming, intelligent radar anti-jamming technologies are urgently needed. Due to the variety of radar electronic counter-countermeasures (ECCMs), it is necessary to efficiently optimize ECCMs in the high-dimensional knowledge base to ensure that the radar achieves the optimal anti-jamming effect. Therefore, an intelligent radar anti-jamming decision-making method based on the deep deterministic policy gradient (DDPG) and the multi-agent deep deterministic policy gradient (MADDPG) (DDPG-MADDPG) algorithm is proposed. Firstly, by establishing a typical working scenario of radar and jamming, we designed the intelligent radar anti-jamming decision-making model, and the anti-jamming decision-making process was formulated. Then, aiming at different jamming modes, we designed the anti-jamming improvement factor and the correlation matrix of jamming and ECCM. They were used to evaluate the jamming suppression performance of ECCMs and to provide feedback for the decision-making algorithm. The decision-making constraints and four different decision-making objectives were designed to verify the performance of the decision-making algorithm. Finally, we designed a DDPG-MADDPG algorithm to generate the anti-jamming strategy. The simulation results showed that the proposed method has excellent robustness and generalization performance. At the same time, it has a shorter convergence time and higher anti-jamming decision making accuracy.