Low Probability of Intercept Radar Signal Recognition by Staked Autoencoder and SVM

Wang, Huali; Zhou, Kaijie; Cao, Peipei

doi:10.1109/wcsp.2018.8555569

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…To demonstrate the recognition performance of the proposed algorithm, we compare the approach with CWD-CNN [8], CWD-MFCNN [17], CWD-ResNet-SVM [14], FSST-CNN [12], and FSST-SAE. The FSST-SAE method uses the enhanced FSST to obtain 64 × 64 T-F images and applies an SAE network [24] with two autoencoder layers for classification. For the network training, we select the Stochastic…”

Section: B Performance Comparisonmentioning

confidence: 99%

“…Authors in [10] applied the ResNet for complex multiple radar waveforms. In addition to the CNN network, the Stacked Auto Encoder(SAE) model had also been applied to radar waveform classification [24]. Besides, some scholars used deep learning networks to extract the features of T-F images and selected the Tree-based Pipeline Optimization Tool (TPOT) or Support Vector Machine (SVM) for classification [9] [14].…”

Section: Introductionmentioning

confidence: 99%

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LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

et al. 2021

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Deep neural networks are used as effective methods for the Low Probability of Intercept (LPI) radar waveform recognition. However, existing models' performance degrades seriously at low Signal-to-Noise Ratios (SNRs) because the effective features extracted by the networks are insufficient under noise jamming. In this paper, we propose a multi-resolution deep feature fusion method for LPI radar waveform recognition. First, we apply the enhanced Fourier-based Synchrosqueezing Transform (FSST), which shows good performance at low SNRs, to convert radar signals into time-frequency images. Then, we construct a multi-resolution deep convolutional network to extract more deep features from each resolution channel. Next, we explore an interactive feature fusion strategy for deep feature fusion. By some down-sampling or up-sampling blocks, different resolution features are fused to generate new features. Finally, we apply a fusion algorithm to the fully connected layer to achieve classification fusion for better performance. Simulation experiments on twelve kinds of LPI radar waveforms show that the overall recognition accuracy of our method can reach 95.2% at the SNR of-8 dB. It is proved that our approach does indeed improve the recognition accuracy effectively at low SNRs. INDEX TERMS radar waveform recognition, multi-resolution, feature fusion, FSST, convolutional neural network.

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Section: B Performance Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

et al. 2021

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“…The basis of each transform domain is t, e jwt , chirp signal, a,b (t), etc. They can be converted into each other, and at the same time, the signals can be processed independently [14], [15]. At present, the methods for digital signal processing based on transformation analysis technology are mainly as follows:…”

Section: Transformation Analysismentioning

confidence: 99%

“…It can be found by Cauchy convergence criteria that there exists x = (k) to make lim n→∞ x (n) (k) = x(k). Just because x = {x(k)} ∞ k=1 , for any r by (14), when m, l > N , there exists…”

Section: Interference Approximation a Hilbert Signal Spacementioning

confidence: 99%

“…In order to improve the efficiency of processing and the rationality of characteristics, transformation analyses, such as the Fourier transforms [7], [8], Fractional order [9]- [11], and wavelet [12], [13], are used to identify inherent characteristics, which makes them more rational. Meanwhile, the corresponding spatialtemporal information, entropy feature, and high-order cumulates [14], [15] are also presented in the interference classification, and the system models are designed for a particular type. Furthermore, improved technologies are used to analyze the sparsity features in some aspects, including sparse decomposition [16], [17] and matching pursuit [18], and achieve the accurate recovery of signals.…”

Section: Introductionmentioning

confidence: 99%

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Classification Methods With Signal Approximation for Unknown Interference

Wang

Huang

et al. 2020

IEEE Access

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There has been growing interest in the classification of interference types in communication systems, especially under large samples and unknown interference, which severely restrict anti-jamming performance of the system. In this paper, we present two signal approximation algorithms for classification restricted different conditions, and the transform learning label consistency (TLLC) is embedded into the evaluation owing to the imperfect performance for classification and feature library. First, the interference signals are converted into the signal feature space, and then the interference processing and feature extraction are conducted based on the Hilbert signal space theory. Second, the projection approximation (PA) for signal approximation is used to approximate the unknown interference, and the restricted projection property is demonstrated as well. Furthermore, in order to ease the restrictions, the sparse approximation (SA) for interference signals is demonstrated. Moreover, an unsupervised learning method and the unknown interference classifier are proposed based on the self-organizing map (SOM) neural network. Based on l1 minimization functions, we improve the accuracy of TLLC with sparse approximation, which is more suitable for general interference signals. Finally, the simulation results demonstrate that, compared with the traditional classification method, the proposed method improves the classification accuracy of known interference by 3.44%. In this case, the overall accuracy is close to that of the supervised learning method, and the speed of processing interference is increased by more than 10 times. When the SNR reaches 5 dB, the accuracy of unknown interference classification exceeds 94%. Finally, yet importantly, owing to the imperfect performance for classification and feature library at present, we acquire the final accuracy for them at 92.23% by intervening measures, and the time availability also has been obtained advantages on signal processing.

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Automatic Modulation Classification of Radar Signal Based on Machine Learning Network

Wang

Jiang

et al. 2021

2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC)

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Low Probability of Intercept Radar Signal Recognition by Staked Autoencoder and SVM

Cited by 7 publications

References 7 publications

LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

Classification Methods With Signal Approximation for Unknown Interference

Automatic Modulation Classification of Radar Signal Based on Machine Learning Network

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