This paper introduced an improved algorithm for deinterleaving of radar pulses, which was based on TOA and frequency. The two_parameter algorithm was applied to classic, frequence_agile and staggered PRI radar signals. It is shown that the new method is successful in high_pulse_density radar environments and for complex signal types.

An Improved Algorithm for Deinterleaving of Radar Pulses

A comprehensive and well-structured review on the application of deep learning (DL) based algorithms, such as convolutional neural networks (CNN) and long-short term memory (LSTM), in radar signal processing is given. The following DL application areas are covered: i) radar waveform and antenna array design; ii) passive or low probability of interception (LPI) radar waveform recognition; iii) automatic target recognition (ATR) based on high range resolution profiles (HRRPs), Doppler signatures, and synthetic aperture radar (SAR) images; and iv) radar jamming/clutter recognition and suppression. Although DL is unanimously praised as the ultimate solution to many bottleneck problems in most of existing works on similar topics, both the positive and the negative sides of stories about DL are checked in this work. Specifically, two limiting factors of the real-life performance of deep neural networks (DNNs), limited training samples and adversarial examples, are thoroughly examined. By investigating the relationship between the DL-based algorithms proposed in various papers and linking them together to form a full picture, this work serves as a valuable source for researchers who are seeking potential research opportunities in this promising research field.

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Deep-Learning for Radar: A Survey

As the electromagnetic environment in battlefields is more and more complex, automatic modulation recognition for radar signals is becoming vital and challenging. Traditional methods are more likely to cause lower recognition accuracy with higher computational complexity in low signal-to-noise ratio (SNR). Feature redundancy especially for handcrafted features is one of the shortcomings of deep-learning-based methods. In this article, a novel end-to-end sequence-based network that consists of a shallow convolutional neural network, a bidirectional long short-term memory (Bi-LSTM) network strengthening with a self-attention mechanism, and a dense neural network is constructed to recognize eight kinds of intrapulse modulations of radar signals. The autocorrelation functions of received radar signals are first calculated as autocorrelation features. Then, these features are employed as inputs of the proposed network which owns significant sequence processing advantages and adaptive selection ability of features. Finally, the proposed network outputs prediction modulations directly. The simulation results verify the robustness and effectiveness of autocorrelation features. And the proposed network achieves about 61.25% accuracy at −20 dB and more than 95% accuracy at −10 dB. Compared with four state-of-the-art networks, the proposed network has better recognition performance especially at low SNRs with much lower computational complexity. Results on measured signals also demonstrate that the proposed network outperforms these four networks.

Self-Attention Bi-LSTM Networks for Radar Signal Modulation Recognition

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.

In this letter, we introduce CGDNet, a cost-efficient hybrid neural network composed of a shallow convolutional network, a gated recurrent unit, and a deep neural network, for robust automatic modulation recognition for cognitive radio services of modern communication systems Our model employs pooling layers, small filter sizes, Gaussian dropout layers, and skip connections which leads to an increase in network capacity, a reinforced process of feature extraction, and prevents the vanishing gradient problem From our experiments, CGDNet incurs a low computational complexity and reaches the overall n-modulation recognition accuracy of 935% and 9038% on two widely used Deep-Sig datasets

CGDNet: Efficient Hybrid Deep Learning Model for Robust Automatic Modulation Recognition

Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different signal-to-noise ratio (SNR) levels from -14 to 20 dB. First, raw signal sequences in the time domain, frequency domain and autocorrelation domain are as input for a shallow CNN. Then the features extracted by CNN will be the input of LSTM network. Finally, DNNs will output the signal modulation types directly. The simulation results demonstrate that the accuracies in autocorrelation domain are all more than 90% at -6 dB and close to 100% when SNR > -2 dB. The recognition performances of the three domains are compared. Compared with other recognition methods, the proposed method has higher average accuracy and better performance under low SNR condition. The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rsn.2019.0436

Intra-pulse modulation radar signal recognition based on CLDN network

In this paper, a novel recognition method based on the squeeze-and-excitation networks (SE-Nets) is proposed in order to recognize intra-pulse modulation signals at varying noise levels automatically. Firstly, different signal transforms including time domain, frequency domain and time–frequency domain are used to convert seven different intra-pulse modulation signals into images. Then, since the SE-Net has great advantages in image processing, the images are classified by the squeeze-and-excitation networks and output the results in each domain. Lastly, the decisions of different domains are combined to obtain the final results. The simulation results demonstrate that the recognition accuracies are all more than 95% except BPSK signals which are still more than 90% at the case of − 8 dB. Compared with several other neural networks and traditional support vector machine method, the accuracy of SE-Net method has increased more than 2% and performs better under different SNR conditions. The measured signals results show that the accuracies of the SE-Net method are higher than those of several other neural networks, especially for BASK and BFSK signals.

Intra-pulse modulation radar signal recognition based on Squeeze-and-Excitation networks

Classification and recognition of radar signals is an important part of modern warfare. Traditional radar signal recognition method based on the pulse description word may be not suitable for the complex electromagnetic environment. In this paper, a novel radar signal recognition method based on Squeeze-and-Excitation networks is proposed. Firstly, time-domain, frequency-domain and time-frequency-domain features of five kinds of radar signals are transformed into images. Then the images are classified by convolutional neural networks and output the result of each domain. Finally, the results of the three domains are combined to obtain the correct identification results. And the recognition effects of three different modules are compared. The experiments prove the efficiency of our method, especially on different SNR condition and center frequency. And confusion matrixes demonstrate that under the low SNR condition, our method still has a good recognition effect and are better than traditional radar signal recognition method based on PDW.

Xiaojun Hao

Papers

Intra-pulse modulation radar signal recognition based on CLDN network

Intra-pulse modulation radar signal recognition based on Squeeze-and-Excitation networks

Radar Signal Recognition Based on Squeeze-and-Excitation Networks

Knowledge distillation-based performance transferring for LSTM-RNN model acceleration