Imaging jamming of inverse synthetic aperture radar (ISAR) has been studied extensively in recent decades. Among them, the periodic interrupted sampling repeater jamming (P-ISRJ) technology can generate a series of false targets through matching filtering. However, with the rapid development of anti-jamming technology, it is necessary to improve the existing periodic sampling method to achieve better jamming effects. The latest research proposes that the two-dimensional square jamming strip can be generated based on the nonperiodic interrupted sampling repeater jamming (NP-ISRJ) to cover the target. In this article, the influence of NP-ISRJ on ISAR is studied with the model of escort free-flight. A linear weighted optimization method is proposed to improve the uneven energy distribution. The experimental results show that a square jamming strip with better blanket effects can be generated under the traditional range Doppler imaging method. While for the compressed sensing imaging method, the imaging fails because the sparse condition is no longer satisfied. The validity of the proposed blanket jamming method in different ISAR imaging reconstruction methods is verified.

Smart noise jamming is a new jamming style against radar, which plays an important role in modern radar electronic warfare. In order to solve the complex problem of convolutional smart noise jamming suppression with different time delay and jamming frequency modulation, a novel jamming suppression method based on the optimized atomic dictionary decomposition by estimating significant jamming parameters is proposed in this paper. First, the dual channel jamming signal information is obtained by designing twinning waveforms, then the independent estimation of time delay and jamming frequency parameters are established based on the spatial position sequences of the false targets. After that, the atomic dictionary is optimized by utilizing the obtained jamming parameters to achieve more effective decomposition. The performance of the new jamming suppression method is analyzed and verified by experimental simulations. The results show that this method can feasibly and practicably suppress the smart noise jamming with a deceptive effect, and the atomic decomposition efficiency is highly improved.

Interrupted sampling repeater jamming (ISRJ) is an attracted coherent jamming method to inverse synthetic aperture radar (ISAR) in the past decades. By means of different jamming parameters settings, realistic dense false targets can be formed around the true target. This paper proposed an adaptive anti-jamming method against ISRJ by adjusting the number of measurements based on compressed sensing (CS). The jamming signal is energy concentrated and segmented sparse in the frequency domain. The measurements number of the reconstructed target signal and the jamming signal is different. According to the restricted isometry property (RIP) condition of CS theory, signal reconstructing performance depends on the number of measurements that varies with the sparsity of the vector. Thus, the jamming signal is suppressed, and the true target signal is retained by altering the measurements number of echo signals. Besides, the two-dimensional (2D) anti-jamming method is derived in detail. The anti-jamming effect is analyzed with different signal-to-noise ratios (SNR), sampling rates, and jam-to-signal ratios (JSR). Simulations prove the effectiveness of the proposed anti-jamming method.

Interrupted sampling repeater jamming (ISRJ) is an effective method for implementing deception jamming on chirp radars. By means of frequency-shifting jamming processing of the target echo signal and pulse compression during image processing, a group of false targets will appear in different spatial locations around the true target. Extracting the features of these false targets is complex and limited to existing countering methods against ISRJ. This paper proposes an anti-jamming method to identify the spatial location characteristics of two-dimensional deception false targets. By adjusting the parameters of the radar transmitted signal, the method simultaneously transmits the anti-jamming signal and carries out false target identification and elimination in the range and azimuth dimensions. Eventually, the optimal signal parameter design of the anti-jamming signal is obtained by comparing different anti-jamming strategies in the range dimension. The validity of the proposed method is proved by deducing the mathematical model between the spatial distribution characteristics of the false targets and the radar transmitted signal parameters and demonstrated by simulations.

Micro-motion jamming is a new jamming method to inverse synthetic aperture radar (ISAR) in recent years. Compared with traditional jamming methods, it is more flexible and controllable, and is a great threat to ISAR. The prerequisite of taking relevant anti-jamming measures is to recognize the patterns of micro-motion jamming. In this paper, a method of micro-motion jamming pattern recognition based on complex-valued convolutional neural network (CV-CNN) is proposed. The micro-motion jamming echo signals are serialized and input to the network, and the result of recognition is output. Compared with real-valued convolutional neural network (RV-CNN), it can be found that the proposed method has a higher recognition accuracy rate. Additionally, the recognition accuracy rate is analyzed with different signal-to-noise ratio (SNR) and number of training samples. Simulation results prove the effectiveness of the proposed recognition method.

Chopping and Interleaving jamming (C&I) is an effective jamming method against linear frequency modulated (LFM) pulse compression radar. In order to suppress the false target generated by C&I jamming, a method based on time-frequency shift recombination is proposed in this paper. Firstly, the jamming signals received are analyzed by Gabor transform, and then the number of chopping and the number of interleaving are estimated. After that, the modulated signals are designed based on the key jamming parameters to carry out time-frequency shift and recombination. Finally, the jamming was suppressed by matched filtering. The simulation results show that the proposed method can effectively utilize the energy of the jamming signal, solve the problem that it is difficult to suppress the jamming when the jamming to signal ratio (JSR) is relatively large, and avoid the complicated signal separation algorithm. When the JSR is 10dB, the signal to jamming plus noise ratio gain can reach more than 20dB after jamming suppression.