With the development of new active deception jamming, radar antijamming has become a major research hotspot, and the recognition of jamming type is one of its key steps. In recent years, deep learning has been successfully applied in the field of radar jamming recognition, such as convolutional neural networks (CNNs). However, it is difficult to effectively improve the accuracy of deep learning algorithms in the case of small sample. Furthermore, ensemble learning and transfer learning can effectively improve the model generalization performance. For the small sample problem, this article proposes a weighted ensemble CNN with transfer learning (WECNN-TL)-based radar active deception jamming recognition algorithm. The main idea of this method is to obtain the time–frequency distribution maps of jamming signals by the short-time Fourier transform (STFT), and then, their real parts, imaginary parts, moduli, and phases are combined differently to construct multiple datasets. Finally, an ensemble CNN (ECNN) model with weighted voting and transfer learning is constructed to realize jamming recognition. Experiments on the simulated and measured mixed datasets (including 12 types of samples) show that the proposed method can get better recognition performance than random forest (RF), support vector machine (SVM), and some CNN-based methods.

Agility radar with the carrier frequency random hopping and the pulse repetition frequency (PRF) staggering from pulse to pulse achieves superior performance against the electromagnetic jamming. This novel scheme leads to the discontinuity of phase in a coherent processing interval, thus the fast Fourier transform‐based method is no longer a valid way to estimate the velocity of a target. A novel sparse optimisation method based on compressed sensing is proposed for high‐resolution range–Doppler reconstruction from random frequency hopping and PRF‐jittering pulses. The performance of moving target detection of the proposed method for frequency agile and PRF‐jittering radar is analysed by comparing it with parameters‐fixed pulse Doppler radar. Both simulation and field experimental results demonstrate the effectiveness of the proposal.

Electromagnetic controlled surface (ECS) which can regulate the amplitude and phase of electromagnetic wave reflection characteristics has attracted extensive attention in recent years because of its radar stealth effect by reducing the radar cross section (RCS). However, radar detection cannot be avoided only by stealth in the energy field gradually. It is a novel pulse Doppler radar jamming mode proposed in this paper to utilize the self-correlation of radar-transmitted waveforms to result in signal processing and radar detection failure through the rapid time-domain variation of phase coding ECS. In principle, phase modulation alters the intra-pulse characteristics of the original signal and fundamentally interferes with the processing of the radar signal. In this paper, random and periodic phase coding sequences are proposed according to different forms of radar jamming. And through the derivation formula and simulation experiment, the jamming effect under multi-phase modulation is verified. Among them, the target energy is dispersed to the surroundings to form a wide envelope through random coding modulation, which leads to noise barrage jamming. While we use periodic sequence coding modulation, the target is shifted in the range-Doppler domain, causing misplaced coherence on the radar echo and deceptive jamming to the radar. Moreover, the technology can also be widely used in synthetic aperture radar (SAR) imaging, microwave measurement and other remote sensing fields. To further confirm the accuracy and efficiency of the proposed method, multi-parameter contrast experiments and parameter sensitivity analyses are conducted.

With the rapid development of electronic countermeasure technology, many active jamming compound scenes pose severe challenges to traditional radar, synthetic aperture radar (SAR), and other detection technologies. The accurate monitoring and recognition of individual jamming types contained in the complex electromagnetic environment can provide valuable prior information for radar countermeasures. However, existing jamming recognition algorithms suffer from huge models, fewer recognizable jamming types, and weak robustness, which is difficult to apply effectively to the resource-constrained airborne pulse signal real-time analysis instruments. This paper proposes a multi-label learning-based lightweight compound jamming recognition algorithm to solve these problems, including three key steps. First, the proposed method performs de-chirp, time-frequency transformation, and grayscale compression preprocessing for radar echoes. Then, an efficient hybrid attention (EHA) mechanism is designed and combined with ShuffleNet v2 to construct a recognition model. Finally, we generate independent multi-label discriminant thresholds based on dual evaluation metrics and a genetic algorithm to improve the recognition effect. The experiment shows that the floating-point operation (FLOPs) of the proposed method is only 0.11% ~ 57.19% of the existing jamming recognition methods, the overall recognition accuracy of the measured jamming data is 92.25%, higher than the existing methods of 7.37% ~ 16.73%, and has strong robustness to the fluctuation of radar waveform parameters.