The development of the digital radio frequency memory (DRFM) has led to the interrupted sampling repeater jamming (ISRJ) becoming increasingly popular in electronic countermeasure (ECM). It is coherent with the emitted signal and extremely limits radar target detection which significantly obstructs radar electronic countercountermeasure (ECCM). In this paper, we study the ISRJ suppression for pseudo random code continuous wave (PRC-CW) radars. First, the relationship between the ISRJ and the radar waveform is obtained by analyzing the ISRJ principle. Second, the intermittent feature of the ISRJ with matched filter sliding is discussed and used to determine the retransmitted sampled slices (RSS). Third, the jamming signal is reconstructed using the minimum residual criterion and excluded from the echo signal. In the proposed method, we make the most of the information of the jamming signal for improving the anti-ISRJ performance in the low SNR regimes. This information pertains to the amplitude of the jamming signal being considerably higher than that of the real target signal. Fourth, utilizing this characteristic of the jamming signal, we propose an improved sliding matched filter method based on the RSS reconstruction. Last, numerical simulations illustrate the effectiveness of the proposed method and validation of the theoretical analysis.

Automotive radars have faced to various jamming signals with different types and parameters. One counter measure is to identify the fine-grained modulation types of these jamming signals, which can provide supplementary information for intercepting. Most existing signal modulation recognition methods attempt to establish a machine-learning mechanism by training with a large number of annotated samples, which is hardly applicable in real-world electronic reconnaissance scenarios where only a few samples can be intercepted in advance. Few-Shot Learning aims to learn from base classes with many samples and infuse this knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, inspired by the fact that the energy distribution of the signal of interest is always concentrated in time-frequency images, a novel few-shot learning framework based on foreground segmentation is proposed, which benefits from its powerful filtering to remove the noise and clutter from background. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust under different signal-to-noise-ratio conditions.

Interrupted sampling repeater jamming (ISRJ) is becoming more widely used in electronic countermeasures (ECM), thanks to the development of digital radio frequency memory (DRFM). Radar electronic counter-countermeasure (ECCM) is much more difficult when the jamming signal is coherent with the emitted signal. Due to the intermittent transmission feature of ISRJ, the energy accumulation of jamming on the matched filter shows a ‘ladder’ characteristic, whereas the real target signal is continuous. As a consequence, the time delay and distribution of the jamming slice can be obtained based on searching the truncated-matched-filter (TMF) matrix. That is composed of pulse compression (PC) results under matched filters with different lengths. Based on the above theory, this paper proposes a truncated matched filter method by the reconstruction of jamming slices to suppress ISRJ of linear frequency modulation (LFM) radars. The numerical simulations indicate the effectiveness of the proposed method and validate the theoretical analysis.

In the presence of steering vector model mismatches, a novel robust adaptive beamformer is proposed based on interference‐plus‐noise covariance matrix reconstruction. An interference region is first selected according to the coarsely estimated desired signal direction, and then the interference steering vectors are estimated with the robust Capon beamformer. Finally, the interference‐plus‐noise covariance matrix is reconstructed by integrating the Capon spatial spectrum over the interference region by using the estimated steering vectors.

For bistatic inverse synthetic aperture radar (Bi-ISAR) imaging of space targets, the impacts of scatterers’ migration through resolution cell (MTRC) and azimuth high-order phase (AHP) are becoming more severe, especially for a long coherent processing interval (CPI). In this paper, the movement of a scatterer is modeled as a quartic function under the restriction of the space target orbit, then an MTRC-AHP compensation algorithm is proposed. A fourth-order keystone transform is introduced to remove the MTRC firstly, then the residual AHP terms are compensated gradually via the azimuth compensation phase and fractional Fourier transform (FRFT). The impact of the orbit prediction error of the space target on the algorithm is analyzed theoretically. Numerical simulations illustrate the effectiveness of the proposed method and validation of the theoretical analysis.

The multi-functional radars (MFRs) can switch among a variety of fine-grained working modes, which often have flexible modulation types and programmable parameters. In an electromagnetic reconnaissance system, the process of identifying different working modes in pulse sequences guarantees the subsequent intention analysis and assists in devising a jamming strategy. Most of the existing working mode recognition methods attempt to establish a machine-learning mechanism by training a model using a large number of annotated samples. However, this is hardly applicable in the real-world scenarios where only a few samples can be intercepted in advance. As the labeled signal samples are expensive, one direction is to augment the dataset by generating either samples or signal features in an embedding space. In this work, inspired by the fact that different modalities of the same working modes are generated from a set of fixed parameters, a few-shot learning framework based on compound alignments is proposed. Three branches, which take observations of long windows, observations of short windows, and coded semantic attributes as inputs, are aligned in both the latent variable space and the reconstruction space to learn a shared embedded space. A simple soft-max classifier is trained by sampling sufficient instances in the learned space to realize the final identification process. The experimental results and analysis show that the proposed method achieves excellent performance for fine-grained working modes recognition even for small number of observed pulses. In addition, the proposed method is robust under different non-ideal conditions, such as noise contamination, incomplete sequences, and spurious pulses.