Convolutional neural network applied to specific emitter identification based on pulse waveform images

Wang, Xuebao; Huang, Gan; Ma, Congshan; Tian, Wei; Gao, Jun

doi:10.1049/iet-rsn.2019.0456

Cited by 11 publications

(10 citation statements)

References 32 publications

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“…In the field of electromagnetic warfare (EW) [1][2], radar emitter recognition (REI) plays an essential role in electronic support measures (ESM) and electronic intelligence (ELINT) systems [3][4] [5] [6]. The ESM system is responsible for understanding the radio frequency environment and providing emission intercept, detections, recognition, characterization, location, and overall situational awareness of emitters [7].…”

Section: Introductionmentioning

confidence: 99%

Intrapulse Recognition of Radar Signals Via Bicubic Interpolation WVD

Wu,

Huang,

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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Section: Introductionmentioning

confidence: 99%

Intrapulse Recognition of Radar Signals Via Bicubic Interpolation WVD

Wu,

Huang,

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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“…With the widespread use of radar in the military and civilian fields, radar countermeasure reconnaissance has become a major form in electromagnetic spectrum operations [1]. Radar waveform recognition is one of the key technologies of the radar countermeasure reconnaissance system [2], and its result is an important basis for battlefield situation awareness, threat estimation and command decision‐making [3]. The traditional method mainly uses the intelligence personnel to manually extract the pulse description word [4], which is matched with the template parameters and can only identify certain signal types.…”

Section: Introductionmentioning

confidence: 99%

Radar waveform recognition based on a two‐stream convolutional network and software defined radio

Xia

Huang

2022

IET Radar Sonar & Navi

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As one of the key technologies of electromagnetic spectrum operations, radar waveform recognition is an important basis for judging the threat degree of enemy’s weapons. At present, it is difficult to recognize the waveform at low signal‐to‐noise ratio (SNR), and the theoretical simulation is used in most research studies, which leads to the problem that the engineering practicality of the algorithm is poor. In this paper, a two‐stream convolutional network radar waveform recognition (TCNRWR) algorithm that fuses the original time–frequency image and the continuous frames autocorrelation time–frequency image is proposed, and an experimental platform is built by using software defined radio technology. Compared with traditional theoretical simulations, this paper focusses on the influence of spatial background interference and signal incompleteness on the recognition of intra‐pulse signals, and proposes an automatic removal method of invalid signal in the time–frequency domain. In order to further improve the recognition rate at low SNR, a deeper feature of short‐time autocorrelation flow is excavated, and a two‐stream time–frequency images fusion convolutional network (TTIFCNet) is constructed. The network effectively fuses the pre‐classification results of the two streams through Softmax layer and different weights. Finally, the hardware platform HackRF is used to realize the communication and processing of signals in real space, which proves the effectiveness of the TCNRWR algorithm.

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“…Deep learning approaches have shown superior performance in recognition and classification tasks [17]. In 2018, Ding et al [18] constructed a convolutional neural network to recognize compressed bispectral features of signals.…”

Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification Based on Multichannel Depth Feature Fusion

Tong

Fang

et al. 2022

Wireless Communications and Mobile Computing

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Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter. This is achieved by processing the radio-frequency signals. Feature extraction and classifier selection are key factors that affect SEI performance. This paper proposes a deep convolutional neural network model based on multisignal feature fusion to identify the emitters. As part of the implementation model, the methods of singular spectrum analysis (SSA), variational mode decomposition (VMD), and intrinsic time-scale decomposition (ITD) are used to extract various signal features of emitter signals. Finally, a multichannel deep learning model is adopted to fuse each signal feature automatically and identify different signal emitters. Experimental results show that the proposed method completely considers the complementarity and independence of varying signal features and excavates hidden deep feature information. Hence, the process is considered reliable and effective.

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Convolutional neural network applied to specific emitter identification based on pulse waveform images

Cited by 11 publications

References 32 publications

Intrapulse Recognition of Radar Signals Via Bicubic Interpolation WVD

Intrapulse Recognition of Radar Signals Via Bicubic Interpolation WVD

Radar waveform recognition based on a two‐stream convolutional network and software defined radio

Specific Emitter Identification Based on Multichannel Depth Feature Fusion

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