Intra‐pulse modulation radar signal recognition based on CLDN network

Wei, Shunjun; Qu, Qizhe; Su, Hao; Wang, Mou; Shi, Jun; Hao, Xin

doi:10.1049/iet-rsn.2019.0436

Cited by 53 publications

(25 citation statements)

References 14 publications

(15 reference statements)

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“…Figure 9 a illustrates that the recognition result of the proposed method precedes the current methods [ 4 , 17 , 18 ] comprehensively under the same SNR. When the SNR is −8 dB, the recognition result of the existing approach is very poor.…”

Section: Simulation Resultsmentioning

confidence: 94%

“…The overall correct recognition rate is over 95% when the SNR is above 2 dB. Literature [ 17 ] transformed raw signal sequences in the autocorrelation domain, and then designed a DCNN to train autocorrelation sequences. This classification system can classify FSK, BPSK, continuous wave (CW), linear frequency modulation (LFM), Sinusoidal frequency modulation (SFM) and QPSK signals.…”

Section: Introductionmentioning

confidence: 99%

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Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Chen

Zhang

Zhu

et al. 2021

Sensors

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Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

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Section: Simulation Resultsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Chen

Zhang

Zhu

et al. 2021

Sensors

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show abstract

“…Intrapulse modulation recognition is a task to classify modulation of frequency or phase within the pulse. Deep learning methods such as CNN and LSTM have been actively applied for intra-pulse modulation [3] [4] [5] and the construction of a separate non-negative matrix factorization network was proposed in [6]. Emitter identification is a task to identify emitters through comparison with a built-in library using parameters extracted through signal analysis.…”

Section: Related Workmentioning

confidence: 99%

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

Han

Park

2021

IEEE Access

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In the modern electronic warfare signal environment, multiple radar signals of high density are mixed and received, and separating them into signals for each emitter is an essential step for emitter identification. Each radar has its own pulse repetition interval (PRI), which is a key parameter for deinterleaving pulse trains. The PRI is modulated in various forms depending on the purpose of the radar operation, and analyzing the mean PRI and the modulation type of PRI is the core of electronic warfare signal processing. Many existing papers have tried separate independent approaches for deinterleaving and for PRI modulation recognition. However, many distortions are unintentionally generated in the process of extracting the pulse train using the PRI estimated through deinterleaving for the PRI modulation recognition. This degrades the the modulation recognition performance. In this paper, we propose a unified method for the deinterleaving and PRI modulation recognition of radar pulses using deep learning-based multitasking learning. The simulation results demonstrate the good performance of the proposed method for deinterleaving and modulation recognition, compared to the conventional method, and prove that the proposed method is robust in noisy radar signal environments.

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“…This method obtained the time-frequency diagram of radar signals through Choi–Williams distribution, then used stacked autoencoders to automatically extract features, and finally completed signal recognition through SVM. Shunjun Wei et al [ 7 ] designed a new type of network combining shallow CNN, LSTM, and deep DNN, which has a good recognition effect on a variety of radar signals. Li ji et al [ 8 ] proposed an IIF-Net deep learning model, which achieved good recognition effect under low SNR.…”

Section: Introductionmentioning

confidence: 99%

A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, Chaotic, EQFM, Frank, FSK, LFM, LOFM, OFDM, P1, and P2. In order to obtain the time-frequency image (TFI) of the multipulse radar signal, the signal is Choi–Williams distribution (CWD) transformed. Aiming at the features of the multipulse radar signal TFI, we designed a distinguishing feature fusion extraction module (DFFE) and proposed a new HRF-Net deep learning model based on this module. The model has relatively few parameters and calculations. The experiments were carried out at the signal-to-noise ratio (SNR) of −14 ∼ 4 dB. In the case of −6 dB, the recognition result of HRF-Net reached 99.583% and the recognition result of the network still reached 97.500% under −14 dB. Compared with other methods, HRF-Nets have relatively better generalization and robustness.

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Intra‐pulse modulation radar signal recognition based on CLDN network

Cited by 53 publications

References 14 publications

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models

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