Zhizhong Zhang

Spaceborne synthetic aperture radar (SAR) working mode recognition is crucial for intention perception against SAR, threat level evaluation, and subsequently guiding the electronic jamming system to protect our key regions. However, as spaceborne SAR develops toward flexibility and intelligence, the agility of its waveforms and beams, as well as the overlap of parameters in different working modes, has posed significant challenges for the rapid and high-accuracy recognition of spaceborne SAR working modes. To improve the ability of spaceborne SAR working mode recognition in complex electromagnetic environments, this article proposes a spaceborne SAR working mode recognition method based on the multiscale Transformer (MFormer) network assisted by SAR semantic information. First, this method introduces SAR semantic information into the model input to mitigate the influence of outliers on recognition performance. Second, a multiscale network is designed to sequentially input tokens of different scales into different layers of the Transformer encoder, fusing multiscale information and enhancing the network’s ability to extract sequence features. Our experiments demonstrate the effectiveness, robustness, and generalization of this method in spaceborne SAR working mode recognition. In the nonideal case of missing pulses and spurious pulses, our method outperforms other methods in terms of accuracy, macro Precision, macro Recall, and macro $F1$ -score. Our method maintains over 80% recognition accuracy under conditions with 20% spurious and missing pulses.

Radar emitter signal sorting (RESS) is a crucial process in contemporary electronic battlefield situation awareness. Separating pulses belonging to the same radar emitter from interleaved radar pulse sequences with a lack of prior information, high density, strong overlap, and wide parameter distribution has attracted increasing attention. In order to improve the accuracy of RESS under scenarios with limited labeled samples, this paper proposes an RESS model called TR-RAGCN-AFF-RESS. This model transforms the RESS problem into a pulse-by-pulse classification task. Firstly, a novel weighted adjacency matrix construction method was proposed to characterize the structural relationships between pulse attribute parameters more accurately. Building upon this foundation, two networks were developed: a Transformer(TR)-based interleaved pulse sequence temporal feature extraction network and a residual attention graph convolutional network (RAGCN) for extracting the structural relationship features of attribute parameters. Finally, the attention feature fusion (AFF) algorithm was introduced to fully integrate the temporal features and attribute parameter structure relationship features, enhancing the richness of feature representation for the original pulses and achieving more accurate sorting results. Compared to existing deep learning-based RESS algorithms, this method does not require many labeled samples for training, making it better suited for scenarios with limited labeled sample availability. Experimental results and analysis confirm that even with only 10% of the training samples, this method achieves a sorting accuracy exceeding 93.91%, demonstrating high robustness against measurement errors, lost pulses, and spurious pulses in non-ideal conditions.

Multi-functional radar (MFR) working mode recognition is a crucial component of analyzing their behavioral intent, serving as a foundation for determining threat levels and making jamming decisions. Most of the existing methods for recognizing working modes are aimed at static observation scenes where all working mode classes are obtained at once. However, it is difficult to effectively adapt to dynamic incremental observation scenes. In this paper, an incremental recognition method for MFR working modes based on deep feature extension (DEF) in dynamic observation scenarios is proposed. This algorithm can continuously update the knowledge of new classes while maintaining the performance of the learned classes through DFE and sample feature playback of old classes. The experimental results indicate that the proposed method can effectively meet the incremental recognition demands of the MFR working mode in a dynamic incremental observation scenario. Moreover, it performs well even under non-ideal conditions like measurement errors, lost pulses, and spurious pulses.

There has been increased interest in recognizing the dynamic and flexible changes in shipborne multi-function radar (MFR) working modes. The working modes determine the distribution of pulse descriptor words (PDWs). However, building the mapping relationship from PDWs to working modes in reconnaissance systems presents many challenges, such as the duration of the working modes not being fixed, incomplete temporal features in short PDW slices, and delayed feedback of the reconnaissance information in long PDW slices. This paper recommends an MFR working mode recognition method based on the ShakeDrop regularization dual-path attention temporal convolutional network (DP-ATCN) with prolonged temporal feature preservation. The method uses a temporal feature extraction network with the Convolutional Block Attention Module (CBAM) and ShakeDrop regularization to acquire a high-dimensional space mapping of temporal features of the PDWs in a short time slice. Additionally, with prolonged PDW accumulation, an enhanced TCN is introduced to attain the temporal variation of long-term dependence. This way, secondary correction of MFR working mode recognition results is achieved with both promptness and accuracy. Experimental results and analysis confirm that, despite the presence of missing and spurious pulses, the recommended method performs effectively and consistently in shipborne MFR working mode recognition tasks.

In the field of target classification, detecting a ground moving target that is easily covered in clutter has been a challenge. In addition, traditional feature extraction techniques and classification methods usually rely on strong subjective factors and prior knowledge, which affect their generalization capacity. Most existing deep-learning-based methods suffer from insufficient feature learning due to the lack of data samples, which makes it difficult for the training process to converge to a steady-state. To overcome these limitations, this paper proposes a Wasserstein generative adversarial network (WGAN) sample enhancement method for ground moving target classification (GMT-WGAN). First, the micro-Doppler characteristics of ground moving targets are analyzed. Next, a WGAN is constructed to generate effective time–frequency images of ground moving targets and thereby enrich the sample database used to train the classification network. Then, image quality evaluation indexes are introduced to evaluate the generated spectrogram samples, with an aim to verify the distribution similarity of generated and real samples. Afterward, by feeding augmented samples to the deep convolutional neural networks with good generalization capacity, the classification performance of the GMT-WGAN is improved. Finally, experiments conducted on different datasets validate the effectiveness and robustness of the proposed method.