Radar jamming recognition aims to accurately recognize the type of jamming to provide guidance for radar countermeasures. Although previous deep learning-based methods have made promising performance, they mainly rely on convolutional neural network (CNN) based on local processing, which ignore the global information in the time-frequency domain data of the jamming signal and also require much inference time to obtain the final recognition results. In this paper, a novel few-shot jamming recognition network via time-frequency self-attention and global knowledge distillation (JR-TFSAD) is proposed by jointly considering the global information in the time-frequency spectrum of the jamming signal and the real-time performance of the recognition network. A time-frequency self-attention model (TFSA) is proposed to extract the global deep features of radar jamming signals by learning the correlation between two arbitrary points in the time-frequency spectrum of the jamming signal, thus improving the recognition accuracy. Moreover, to effectively reduce the inference time of the method while preserving the recognition accuracy, a global knowledge distillation model (GKD) is further constructed to perform jamming recognition by distilling the global knowledge from the TFSA model. The experimental results on the simulated and measured mixed dataset verify that the proposed method has higher recognition accuracy and shorter inference time compared to the state-of-the-art methods.

Randomized stepped-frequency (RSF) radar, which transmits random-frequency pulses in a coherent processing interval (CPI), has excellent electronic counter-countermeasures (ECCM) performance and the ability to synthesize wide frequency band with a narrow bandwidth receiver. However, due to the use of difierent frequency pulses, RSF waveform is incompatible with conventional moving-target-detection (MTD) technique, which makes clutter suppression a dificult problem for RSF radar and limits its application. In this paper, the defect of clutter on the coherent processing for RSF radar is first analyzed. The result shows that coherent processing can improve the signalto- clutter ratio (SCR) on target Doppler channel by N (number of pulses in one CPI) times, which suppresses weak clutter scatterers to a large extent whilst strong clutter scatterers would still be left influential. Focusing on dominant clutter scatterers, the adaptive range-Doppler clutter suppression (ARD-CS) algorithm, which is based on designing clutter suppression filters with twodimensional property to obtain the clear HRRP of moving target in clutter environment, is then proposed for RSF radar. The proposed algorithm, which takes into account clutter Doppler extension, can be used for RSF radar coherent processing in clutter environment.

Sharing a multiple-input multiple-output (MIMO) radar, the single platform can achieve dual functions of radar and communication (DFRC) within the same frequency spectrum, via the same transmit waveforms. In this paper, a space-time coding scheme is developed for transmit beamforming of DFRC and embedding communication information, without their cross-interference. For transmit beampattern design of DFRC, the shape approximation and integrated power approximation criteria are adopted respectively for waveforms optimization with the constant-envelope constraints of transmit waveforms and the equivalent signal in the communication direction. Based on the space-time coding scheme, the direct constellation mapping (DCM) and phase-rotation constellation mapping (PRCM) methods are proposed to embed information symbols. It turns out that the proposed space-time coding scheme for information constellation mapping can prevent missing information symbols and have better performance in bit-error rate (BER), compared to the existing information-embedding techniques. Moreover, the scheme can reduce the dependence of the communication data rate on radar pulse repetition frequency (PRF). Simulation results are presented to demonstrate the effectiveness of the proposed methods.

Effective recognition of radar jamming is of great importance in improving radar system's anti-jamming capability. Existing radar jamming recognition methods based on convolutional neural networks suffer from limitations such as low recognition accuracy and poor network stability in complex electromagnetic environments. This is due to incomplete extraction and utilization of useful information from the jamming signal. In this paper, a few-shot adaptive confidence aggregation and cross-modal refinement jamming recognition method (JR-ACAR) is designed to mine the complete information of the jamming signal for enhancing both accuracy and robustness of the method. Adaptive confidence aggregation (ACA) model is proposed to fully exploit the complementary information between the modulus, phase, real and imaginary parts of the input time-frequency spectrum of jamming signal, and adaptively aggregate these information by confidence vectors. Moreover, to further improve the recognition accuracy while enhancing the robustness of the network, a cross-modal refinement model (CMR) is proposed to mine the correlation information between the aggregated jamming recognition result and the original input time-frequency image. Experimental results on both simulated and measured mixed datasets validate that the proposed JR-ACAR is superior to existing methods for radar jamming recognition in terms of recognition accuracy and robustness.

Randomized stepped-frequency (RSF) radar, which transmits pulses with random carrier frequencies in a coherent processing interval, has an excellent electronic counter-countermeasures performance and the ability to generate the high-range-resolution profiles (HRRPs) of target with a narrow bandwidth receiver. Due to the random nature, the choice of carrier frequencies in RSF radar is usually an undersampling of available frequencies, which make the HRRP generated by matched filtering suffer from high noise-like sidelobes. In this article, we take the sinc-envelopes formed by linear-frequency-modulation pulses as well as RSF phases into account, and propose a novel (or generalized) range sidelobe suppression filters design method for RSF chirp waveform based on convex optimization (the model is transformed into a second-order cone program). By using these filters, the target HRRP with much lower sidelobes can be obtained, and the performance can be traded with the instantaneous bandwidth of the receiver, which provides a new idea for solving the sidelobe problem in RSF radar.

Dynamic analysis of in-orbit satellites is of interest for space situation applications. Some exploratory methods have been presented to analyze the state of attitude-steadied satellites in the complex space situation, but effectively estimating the dynamic parameters of spinning satellites remains a significant and interesting challenge nowadays. With decoupling target spin estimation from its trajectory motion, this paper proposes a novel dynamic estimation approach by using multiple-station inverse synthetic aperture radar (ISAR) images. In brief, quadratic phase characteristic, which is usually regarded as a negative factor for ISAR imagery in conventional algorithms, is now directly exploited to build an explicit expression of the relative motion between the spin in-orbit satellite and radar. Then, based on the developed image refocusing algorithm, these phase coefficients are extracted from the multiple-station images and substituted into the optimization of target dynamic estimation. In the end, target dynamic parameters, including its instantaneous attitude vector and spin speed, are estimated through particle swarm optimization. Experiment results illustrate the feasibility of the proposed algorithm. To some degree, this work also reflects the advantages of multiple-station ISAR system in some space applications.