Automatic Modulation Classification of LFM and Polyphase-coded Radar Signals

Hanbali, Samer Baher Safa; Kastantin, Radwan

doi:10.13164/re.2017.1118

Cited by 8 publications

(3 citation statements)

References 15 publications

(20 reference statements)

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“…In the actual channel, the signal power will gradually fade with the transmission distance, resulting in SNR reduce to -10dB or even lower [20]. In the electronic detection, the signal received by the detector may be sent by the sidelobe of the transmitter, resulting in the received signal with a low SNR [21]. In modern electronic warfare, the communication signal has low power for communication security, and multiple types of noise are often superimposed during wireless propagation, resulting in SNR decreased significantly [22].…”

Section: Introductionmentioning

confidence: 99%

Modulation Recognition Based on Denoising Bidirectional Recurrent Neural Network

Du,

Liu,

Zhang

et al. 2023

Wireless Pers Commun

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Modulation recognition is an important research area in wireless communication. It is commonly used in both military and civilian domains, such as spectrum detection and interference identification. Most existing modulation recognition algorithms have a better recognition performance at high signal noise ratio (SNR). However, when SNR decreases to -10 dB or even lower, such as the battlefield and disaster areas and other harsh environment, the recognition rate may decrease dramatically. In order to solve this problem, a modulation recognition algorithm based on denoising bidirectional recurrent neural network (DBRNN) is proposed. Firstly, the state memory ability of the signal reconstruction layer in the network is utilized to learn the temporal correlation of the modulated signal, the reconstruction of the received signal is completed and the noise in the modulated signal is suppressed. Then, the reconstructed signal is encoded and decoded by the feature reconstruction layer, in which the feature of reconstructed signal is compressed and reconstructed, thereby the influence of noise can be further reduced. Finally, the reconstructed features are identified and classified by the fully connected layer. Simulation results demonstrate that the proposed network can effectively suppress the noise in the signal. Compared with other existing algorithms, the proposed method has higher recognition accuracy in the low SNR environment.

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Section: Introductionmentioning

confidence: 99%

Modulation Recognition Based on Denoising Bidirectional Recurrent Neural Network

Du,

Liu,

Zhang

et al. 2023

Wireless Pers Commun

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“…Automatic radar modulation recognition of low probability of intercept (LPI) radar is one of the core functions required in different kinds of electronic warfare (EW) systems, such as electronic support and electronic intelligence (ES/ELINT), electronic protection (EP), and electronic attack (EA) systems [1][2][3]. As the signal pulse density and complexity increase, the radar receivers may receive multiple radar signals simultaneously [4][5][6][7][8]. Therefore, how to effectively recognize hybrid radar signals has become considerably more crucial.…”

Section: Introductionmentioning

confidence: 99%

Multi‐label hybrid radar signal recognition based on a feature pyramid network and class activation mapping

Luo

Deng

2022

IET Radar Sonar & Navi

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The electromagnetic environment of modern battlefields becomes increasingly complex, and radar receivers may receive multiple radar signals simultaneously. However, current deep learning models can only predict a single class and cannot recognize multi‐label mixed radar signals. In this study, a multi‐label hybrid radar signal recognition framework based on the feature pyramid network (FPN) and class activation map (CAM) is proposed. The multi‐label radar signals are recognized by calculating the average value of the CAM corresponding to each class. The proposed method can recognize, localize and separate mixed radar signals in time‐frequency images, which improves the interpretability and transparency of the model. In addition, the FPN is adopted to improve the spatial resolution of the feature maps, and the Mixup data augmentation is utilized to improve the generalization performance of the model. Experiments with eight different modulation types of mixed radar signals show that the recognition accuracy of hybrid radar signals achieves 92.2% at 0 dB.

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“…It directly affects the accuracy of subsequent tasks, such as electronic countermeasures [1]. The traditional technology of modulation recognition relies on feature engineering and classifier models, such as the pulse description word (PDW) based recognition method [2], artificial feature extraction and classification [3]- [5], and the combined approach of nonlinear feature extraction and machine learning classification [6]- [11]. These traditional methods have been widely applied in this field and have obtained many achievements.…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Modulation Recognition Based on Deep Joint Learning

Dongjin¹,

Yang²,

Li³

et al. 2020

IEEE Access

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The development of integrated avionics systems and electromagnetic spectrum technology has attracted widespread attention. It has further increased the performance requirements for modulation signal recognition technology in complex electromagnetic environments. Therefore, this paper proposes a deep joint learning technique, including deep representation and low-dimensionality discrimination, to enhance feature stability and environmental adaptability. Specifically, we design a feature learning network based on AlexNet to extract in-depth features and optimize it through parameter-based transfer learning techniques, promote multi-level representation capabilities of features and reduce the sample size requirements. Moreover, we propose a classification algorithm based on kernel collaborative representation and discriminative projection to enhance the ability of low-dimensionality representation and between-class discrimination, which optimized using the mini-batch random gradient descent method. As shown in the simulation, the overall average recognition success rate of this method aiming at twelve radar signal modulation types reaches 97.58% at SNR of −6dB. The results of simulation and analysis demonstrate the superiority of the proposed model in terms of robustness, timeliness, and adaptability to small samples.

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Automatic Modulation Classification of LFM and Polyphase-coded Radar Signals

Cited by 8 publications

References 15 publications

Modulation Recognition Based on Denoising Bidirectional Recurrent Neural Network

Modulation Recognition Based on Denoising Bidirectional Recurrent Neural Network

Multi‐label hybrid radar signal recognition based on a feature pyramid network and class activation mapping

Radar Signal Modulation Recognition Based on Deep Joint Learning

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