Automatic Radar Waveform Recognition Based on Deep Convolutional Denoising Auto-encoders

Zhou, Zhiwen; Huang, Gan; Chen, Haiyang; Gao, Jun

doi:10.1007/s00034-018-0757-0

Cited by 40 publications

(30 citation statements)

References 14 publications

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“…In [9,13,14,17], the initial input of the network is one TFI, while this paper proposes inputting three feature images represented by the same signal. Structure 1 is to extract pixel information of three feature images by using three sets of parallel CNN networks, as shown in Figure 12a.…”

Section: Design Of Feature Extraction Structurementioning

confidence: 99%

“…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].…”

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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: Design Of Feature Extraction Structurementioning

confidence: 99%

mentioning

confidence: 99%

LPI Radar Waveform Recognition Based on Features from Multiple Images

Huang

Lin

2020

Sensors

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“…Training is conducted by minimizing the loss function with stochastic gradient descent (SGD). x CNN can be formulated as [20],…”

Section: Senmentioning

confidence: 99%

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

Zhou¹,

Zhang²,

Zhang³

2019

PIER M

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Aimed at the deficiency of conventional parameter-level methods in radar specific emitter identification (SEI), which heavily rely on empirical experience and cannot adapt to the waveform change, a novel algorithm is proposed to extract specific features and identify in Hilbert-Huang transform domain. Firstly, 2-dimensional physical representation of emitters is formed with Hilbert-Huang transform (HHT). Based on this, 4 types of multi-view features are constructed, and the feature space is spanned by elaborating the extraction. Principal components, between-class similarity, spectrum entropy, and deep architecture are used to describe the subtle features. Finally, support vector machine (SVM) is selected as the classifier to realize identification to alleviate the small sample problem. Experimental results show that the proposed algorithm realizes specific identification using 4 intentional modulations of simulated data. The selected 4 types of unintentional representations are feasible to discriminate identical emitters. Additionally, the proposed algorithm obtains higher accuracy than typical signal-level methods in the signal-to-noise ratio (SNR) range [0, 20] dB.

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“…With the rise of deep learning techniques, more and more researchers have applied CNN and DBN in the radar emitter identification task, which achieves good performance. Zhou Z et al [5] developed a novel deep architecture for automatic waveform recognition, which outperformed the existing shallow algorithms and other hand-crafted, feature-based methods. Cain L et al [6] investigated an application of convolutional neural networks (CNN) for rapid and accurate classification of electronic warfare emitters.…”

Section: Introductionmentioning

confidence: 99%

An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

et al. 2019

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Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine (SVM) model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then, we design a semi-supervised co-training algorithm using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Simulation experiments show that the proposed method can effectively identify an unknown radar emitter and still maintain high identification accuracy within a certain measurement error range.

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Automatic Radar Waveform Recognition Based on Deep Convolutional Denoising Auto-encoders

Cited by 40 publications

References 14 publications

LPI Radar Waveform Recognition Based on Features from Multiple Images

LPI Radar Waveform Recognition Based on Features from Multiple Images

Specific Emitter Identification via Feature Extraction in Hilbert-Huang Transform Domain

An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

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