Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network

Qu, Zhiyu; Mao, Xiaojie; Deng, Zhongliang

doi:10.1109/access.2018.2864347

Cited by 80 publications

(79 citation statements)

References 22 publications

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“…Nowadays, the methods that are based on the deep learning have a better performance in radar waveform recognition [14,21]. The overall block diagram of LWRT proposed in this paper is shown in Figure 1.…”

Section: Proposed Lwrtmentioning

confidence: 99%

“…The transformation used in this paper is the Choi-illiams distribution (CWD) [14,21]. When applied to a signal x(t), the time-frequency transformation with bilinear form is expressed as…”

Section: Tfa Technique For Cwd-tfimentioning

confidence: 99%

“…Autocorrelation processing of the signal can effectively filters out the noise [21]. Given a signal x(t) and zero-mean Gaussian white noise w(t), the observable signal is expressed as y(t) = x(t) + w(t), and the autocorrelation function of y(t)…”

Section: Signal Pre-processing For Noise Reductionmentioning

confidence: 99%

“…Researchers have dedicated their attention to image pre-processing, generally in two ways, in order to solve this problem. First, before time-frequency conversion, algorithm filtering is used to reduce noise [21][22][23][24]; second, some image processing technology is used to process TFI to achieve accurate classification [6,13,15,16]. For the first method, most of the current noise reduction algorithms are difficult to apply in a low SNR environment.…”

mentioning

confidence: 99%

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LPI Radar Waveform Recognition Based on Features from Multiple Images

Huang

Lin

2020

Sensors

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Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.Sensors 2020, 20, 526 2 of 23 some existing feature extraction methods are highly targeted, mainly for some specific radar emitter signals. For another thing, these methods rarely involve problems with noise effects and low SNR. In fact, radar signals, especially LPI radar signals, are inevitably subject to large amounts of noise during propagation and reception [5], and LPI radars usually have lower power and it is difficult to directly classify [6]. In current research, there have been some LPI waveform recognition technologies (LWRT), which use feature extraction and classification techniques. Time-frequency analysis (TFA) is widely used in the feature extraction since LPI radar signals are usually non-stationary signals, such as Smoothed Pseudo-Wigner Distribution (SPWD) [7], Wigner Ville Distribution (WVD) [8], Short-Time Fourier Transform (STFT) [9][10][11], and Choi-Williams Distribution (CWD) [6,[12][13][14][15][16]. Combined with deep learning in the field of computer vision [17] and models of neural network structures, researchers have obtained better recognition results from the time-frequency feature of signals [18]. The radar signal is first time-frequency transformed into a two-dimensional time-frequency image (TFI), which then is preprocessed and sent to a neural network for training. In the area of classifier design, classification methods include multi-Layer Perceptron (MLP) [11], conditional decision for different features [11,15], Convolutional Neural Networks (CNN) [14], Elman Neural Networks (ENN) [6], and support vector machines (SVM) [6,16]. In addition, there have been hy...

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Section: Proposed Lwrtmentioning

confidence: 99%

“…The transformation used in this paper is the Choi-illiams distribution (CWD) [14,21]. When applied to a signal x(t), the time-frequency transformation with bilinear form is expressed as…”

Section: Tfa Technique For Cwd-tfimentioning

confidence: 99%

Section: Signal Pre-processing For Noise Reductionmentioning

confidence: 99%

mentioning

confidence: 99%

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LPI Radar Waveform Recognition Based on Features from Multiple Images

Huang

Lin

2020

Sensors

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“…Figure 2 shows the results of the CWD transformation of the twelve LPI radar signals. We found a way to improve the speed of CWD calculations in [15]. In this paper, 1024 × 1024 CWD points are selected.…”

Section: Choi-williams Distributionmentioning

confidence: 99%

LPI Radar Waveform Recognition Based on CNN and TPOT

2019

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The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB.

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An efficient deep convolutional neural network with features fusion for radar signal recognition

Wan

Deng

2022

Multimed Tools Appl

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Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network

Cited by 80 publications

References 22 publications

LPI Radar Waveform Recognition Based on Features from Multiple Images

LPI Radar Waveform Recognition Based on Features from Multiple Images

LPI Radar Waveform Recognition Based on CNN and TPOT

An efficient deep convolutional neural network with features fusion for radar signal recognition

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