Recently, interrupted sampling repeater jamming (ISRJ) based on digital radio frequency memory (DRFM), which could easily form high-power false targets, has severely impacted radar detection capabilities. Conventional anti-ISRJ techniques ignore the operating characteristics of the jammer. Once the anti-ISRJ algorithm and the features of the jammer are mismatched, undesirable jamming residues or signal loss appear. In order to avoid these unwanted performance losses, an algorithm jointing the jam perception and the adaptive waveform is developed in this paper. First, a bi-directional Constant False Alarm Rate (CFAR) detector is introduced to locate and estimate the jamming component in radar echo. An ISRJ feature vector is then built to reconstruct and cancel jamming components for accurate detection under ISRJ. Second, an adaptive anti-ISRJ waveform is developed based on the vector, leading the DRFM to retransmit signals orthogonal to the real detection signal. Besides, the particle swarm optimization (PSO) algorithm efficiently works out the optimal waveform parameters to improve the suppression performance. As radar switches to the adaptive anti-ISRJ waveform, long-term suppression of ISRJ is achieved without locating, estimating, and suppressing the ISRJ components in each echo. Simulated experiments demonstrate that the suggested algorithm can accurately detect radar targets under ISRJ.

In recent years, deep neural networks are increasingly popular in the field of radar high-resolution range profiles (HRRPs) target recognition. Unfortunately, recent researches have revealed that a deep-learning classifier can be easily fooled by adding small perturbations to the input, named adversarial attack. This provides us an inspiration for radar deceptive jamming signal generation in electronic countermeasures (ECMs). However, the perturbations generated by these adversarial attacks are usually of complex envelopes and quite low power, making it challenging for jammers to generate such actual jamming signals. To solve that issue, we propose a practical deceptive jamming generation method that learns the vulnerable range cells in an HRRP sample and injects several jamming pulses with specific amplitudes into these range cells. Such jamming signals are easy to generate and can deceive the radar automatic target recognition (RATR) model to output the wrong target category prediction with high confidence. To avoid the requirement of the recognition network structure information, we leverage the differential evolution optimization algorithm (non-gradientbased). Further, to provide the potential of real-time jamming signal generation during the test, an encoder is constructed not only to learn the separable features but also to find the vulnerable range cells and the specific amplitudes of the jamming pulses. In the experiments, we apply the proposed attack algorithms to fool the one-dimensional convolutional neural network-based HRRPRATR models. The extensive experimental results on measured aircraft HRRP dataset prove that the proposed algorithms achieve a promising attack performance and serve as a practical and fast deceptive jamming generation method.

Recognition of multifunction radar (MFR) working modes provides essential information to assess the behavior of a non-cooperative MFR. However, it typically depends on substantial previously intercepted waveforms. To enable better recognition with limited pre-observations, this paper proposes a radar-semantics-modeled LSTM recognition algorithm with embedding radar waveform functionality features and utilizing contextual recognition of an LSTM network. On the one hand, unsupervised hierarchical modeling and functional semantics embedding extract MFR behavior units and embed the artificial functional semantics of waveforms based on experts’ knowledge about waveform functionalities. On the other hand, an LSTM network captures static and dynamic features within sequences of radar semantic data, recognizing the MFR working mode. Experimental comparisons on preset datasets illustrate that the proposed algorithm adapts more effectively to parameter variations and migrates the recognition between the MFRs with similar functional behavior features.

For the preservation of low-airspace security, a comprehensive threat assessment of low-altitude slow small (LSS) flying targets is essential. Threat assessment intrinsically depends on effectively extracting the optimal attributes from tracks, which represent the target’s motion intention and specific characteristics. However, unavoidable measured noise and track jitters during observation impact assessment performance. To address this problem, long short-term memory (LSTM) is introduced due to its good performance on sequential perception. LSTM captures the permanent intent traits and reduces noise interference utilizing temporal correlations within its novel “memory’’ units. Compared to traditional algorithms, the proposed technique substantially reduces measurement noise, enhancing the accuracy of threat evaluations. Simulation experiments confirm the effectiveness and robustness of the proposed algorithms.

Interrupted sampling repeater jamming (ISRJ) is an effective way to suppress normal radar probing in modern electronic warfare, and ISRJ suppression is essential in the radar signal processing stage. Accurately locating the position of the jamming signal is the first step to suppress the jamming signal. However, the traditional constant false alarm rate (CFAR) has performance loss with the characteristics of intensive distribution, extensive dynamic power range, and non-periodical transmitting. This paper proposes an ISRJ detection method based on a fusion transformer and CFAR. First, the feature extraction model based on the transformer is built to extract the continuous distribution features of ISRJ, forming rough detection results. Then, a CFAR detector is derived based on the rough detection result to estimate detector parameters. Thirdly, the jamming features obtained by the transformer, CFAR detector, and other features (like instantaneous power, average power, and so on) are fused by a decision tree to realize robust detection in a low jamming-to-signal noise ratio. Finally, the simulated experiments to verify the effectiveness of the proposed method.

The signal-level emitter individual identification can provide more accurate and abundant information for radar reconnaissance, interpretation, and combat capability judgment. Unintentional modulation on pulse (UMOP) caused by the differences in the transmitter hardware is distinctive to each emitter. Thus, it is believed as a potential approach to realizing specific emitter identification (SEI) for radar. However, the approach would still meet many issues, like the UMOP is subtle and covered by noise. Meanwhile, such a “fingerprint” exhibit an irregular fluctuation pattern, so it is hard to build a standard and unified feature extraction procedure. In this article, to avoid the influence of pulse modulation on the fingerprint features, we only use the fluctuation of the envelope power to realize SEI. In particular, a probability derivation is demonstrated, and it is concluded that the normalized power of UMOP obeys the complex noncentral chi-square (CNCX) distribution, which establishes a theoretical basis for SEI through UMOP in power. Based on it, a high-precision reconstruction of the UMOP template through several noisy samples is accomplished. Afterward, a Bayesian classifier is naturally built based on the reconstructed UMOP template, which is highly explainable and retains almost the complete distinguishing information of individuals in power. Simulation experiments show that the Bayesian classifier achieves a satisfying accuracy on radar SEI under a low signal-to-noise ratio (SNR) environment. Also, it has a significant advantage over the deep neural network (DNN) under both conditions where sufficient and few-shot training samples are assumed.