As electromagnetic environments in battlefields are more and more complex, there are more kinds of suppression jamming noise including both single jamming signals and compound jamming signals. Thus, suppression jamming recognition especially for compound jamming signals is becoming vital and challenging. Whereas conventional methods are prone to owning the low recognition accuracy and the high computational complexity, especially under low jamming-to-noise ratio (JNR) conditions. In this paper, a novel jamming recognition network (JRNet) based on robust power-spectrum features is proposed to recognize ten kinds of suppression jamming signals including four single jamming patterns and six compound jamming patterns. The proposed method combines the significant representative power of the JRNet and distinguished power-spectrum features of jamming signals to promote the recognition performance. By integrating residual blocks and asymmetric convolution blocks, the JRNet is capable to address the degradation problem and enhance the recognition ability for subtle features. The simulation results show that the overall recognition accuracy of the proposed method is more than 90% even when the JNR is −18 dB and is close to 100% at −6 dB. Compared with five comparison methods in recent literatures, the proposed JRNet achieves better and stable recognition performance especially under low JNR conditions with relatively less storage source and a bit more FLOPs and inference time.

JRNet: Jamming Recognition Networks for Radar Compound Suppression Jamming Signals

Jamming is a big threat to radar system survival and anti-jamming is a part of the solution. The classification of radar jamming signal is the first step toward to anti-jamming. Recently, as an important part of deep learning, convolutional neural network (CNN) based methods have shown their capability in discriminant feature extraction and accurate classification. In this study, in order to harness the powerfulness of deep learning, CNN based methods are proposed to classify radar jamming signal acting on pulse compression radar. Specifically, a 1D-CNN is designed for radar jamming signal classification under the condition of sufficient training samples. Furthermore, due to the fact that the collection of sufficient training samples is time-consuming and expensive, a CNN-based siamese network is proposed for radar jamming signal classification to deal with the issue of limited training samples. The experimental results with sufficient and limited training samples show that the CNN-based classification methods obtain good classification performance in terms of classification accuracy and show a huge potential for radar jamming signal classification.

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Convolutional Neural Network-Based Radar Jamming Signal Classification With Sufficient and Limited Samples

The accurate classification of radar jamming signal is a core step of anti-jamming Recently, convolutional neural network (CNN) based methods have shown their powerfulness in signal processing In this paper, a deep fusion method based on CNN is proposed to classify jamming signal acting on pulse compression radar The proposed method consists of three subnetworks (ie, 1D-CNN, 2D-CNN, and fusion network) 1D-CNN is used to extract deep features of original radar jamming signal Meanwhile, in order to extract the time-frequency features, short time Fourier transform (STFT) is applied to jamming signal to obtain time-frequency spectrograms Then, 2D-CNN is used to extract deep time-frequency features, which are useful for further features fusion processing Fusion network is used to deeply fuse the extracted features of the aforementioned CNNs and softmax is used to finish the task of radar jamming signal classification In addition, in order to alleviate the problem of overfitting and improve the generalization ability of proposed model, soft label smoothing is proposed The experimental results reveal that the proposed method provides competitive results in term of classification accuracy

Deep Fusion for Radar Jamming Signal Classification Based on CNN

Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing.

Jamming is a big threat to the survival of a radar system. Therefore, the recognition of radar jamming signal type is a part of radar countermeasure. Recently, convolutional neural networks (CNNs) have shown their effectiveness in radar signal processing, including jamming signal recognition. However, most of existing CNN methods do not regard radar jamming as a complex value signal. In this study, a complex-valued CNN (CV-CNN) is investigated to fully explore the inherent characteristics of a radar jamming signal, and we find that we can obtain better recognition accuracy using this method compared with a real-valued CNN (RV-CNN). CV-CNNs contain more parameters, which need more inference time. To reduce the parameter redundancy and speed up the recognition time, a fast CV-CNN (F-CV-CNN), which is based on pruning, is proposed for radar jamming signal fast recognition. The experimental results show that the CV-CNN and F-CV-CNN methods obtain good recognition performance in terms of accuracy and speed. The proposed methods open a new window for future research, which shows a huge potential of CV-CNN-based methods for radar signal processing.

Fast Complex-Valued CNN for Radar Jamming Signal Recognition

Radar application in modern warfare becomes more and more rigorous because of the rapidly developed radar countermeasures, especially active jamming in recent years. It costs a radar many resources for anti-jamming in order to detect a target. Hence, it is of great value to recognise the active jamming and thereafter take measures to distinguish target from the numerous jamming. Traditional methods of recognition jamming are blamed for its low efficiency and low accuracy. Radar researchers are looking forward to a new way to do the recognition work. Machine learning has made great advancements in many areas such as image classification, language translation, signal processing and many other recognition tasks, due to its great performance and high accuracy. The authors applied a machine learning method, i.e. convolutional neural networks, to recognise active jamming here. The authors’ results demonstrate that convolutional neural networks have strong ability to distinguish active jamming and thus provide them adequate preparation for anti-jamming process.

Recognition of radar active-jamming through convolutional neural networks

Clinical observation on the benefits of antidepressant intervention in advanced cancer patients

Objective To compare the efficacy and safety of metformin, glyburide, and insulin for GDM, we conducted a subgroup analysis of outcomes for women with GDM according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) diagnostic criteria. Methods We searched the NCBI, Embase, and Web of Science databases from inception to March 2022. Randomized controlled trials (RCTs) that compared the outcomes of hypoglycemic agents in women with GDM were included. Bayesian network analysis was employed. Results A total of 29 RCTs were included. Metformin was estimated to lead to a slight improvement in total gestational weight gain (WMD – 1.24 kg, 95% CI −2.38, −0.09), a risk of unmet treatment target in the sensitivity analysis (OR 34.50, 95% CI 1.18–791.37) than insulin. The estimated effect of metformin showed improvements in birth weight than insulin (WMD – 102.58 g, 95% CI −180.45 to −25.49) and glyburide (WMD – 137.84 g, 95% CI −255.31 to −25.45), for hypoglycemia within 1 h of birth than insulin (OR 0.65, 95% CI 0.47 to 0.84). The improvement in the estimated effect of metformin for hypoglycemia within 1 h of birth still existed when compared with glyburide (OR 0.41, 95% CI 0.26 to 0.66), whether in the IADPSG group (OR 0.33, 95% CI 0.12 to 0.92) or not (OR 0.43, 95% CI 0.20 to 0.98). Conclusion Metformin is beneficial for GDM women to control total GWG compared with insulin, regulate fetal birth weight more than insulin and glyburide, and increase the risk of unmet treatment targets compared with insulin. Compared to metformin, glyburide is associated with neonatal hypoglycemia.

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Boye Sun

Papers

Recognition of radar active-jamming through convolutional neural networks

Clinical observation on the benefits of antidepressant intervention in advanced cancer patients

Efficacy and safety of hypoglycemic agents on gestational diabetes mellitus in women: A Bayesian network analysis of randomized controlled trials