Radar signal feature extraction based on wavelet ridge and high order spectral analysis

Mingqiu, Ren; Jinyan, Cai; Zhu, Yuanqing; Han, Jiawei

doi:10.1049/cp.2009.0123

Cited by 7 publications

(4 citation statements)

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“…domain [6]. For the time domain, Ren et al [7] used high-order statistics and wavelet ridge to extract signal features for recognition. Iglesias et al [8] distinguished signal types by calculating various signal characteristics such as frequency peak and variance based on instantaneous phase and normalized instantaneous frequency of signals.…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Recognition Based on Dual-Channel Model With HOG Feature Extraction

Tang

Quan

Jin

et al. 2023

IEEE J. Miniat. Air Space Syst.

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Objectives: To improve the recognition accuracy of radar signals under a low signal-to-noise ratio (SNR). Technology or Method: We propose a novel radar signal recognition method based on a dual-channel model with the histogram of oriented gradients (HOG) feature extraction. Specifically, multisynchrosqueezing transform (MSST) and Choi-Williams distribution (CWD) transform are adopted individually to obtain the time-frequency distribution images of radar signals, and HOG feature extraction is performed on the preprocessed time-frequency images of each channel respectively. Then, the features of the two channels are fused and dimensionally reduced by the principal component analysis (PCA). Finally, the compact feature parameters are fed to the support vector machine (SVM) classifier to identify radar signals. Clinical or Biological Impact: The experimental results demonstrate that the proposed model achieves a high recognition performance with a small computational complexity, especially in low SNR. When the SNR is -12 dB, the recognition accuracy can reach more than 92%, which is over 6% higher than that of single-channel models and related CNN-based models.

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Section: Introductionmentioning

confidence: 99%

Radar Signal Recognition Based on Dual-Channel Model With HOG Feature Extraction

Tang

Quan

Jin

et al. 2023

IEEE J. Miniat. Air Space Syst.

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“…Early work on radar signal recognition heavily relied on expert knowledge and manual feature extraction [8]. For most feature extraction methods, features are typically obtained in different transformation domains, such as instantaneous features, higher-order cumulants, integrated second-order phase functions, and time-frequency features [9,10]. However, the extraction of signal features traditionally relies on specialized expertise, which restricts its broader applicability.…”

Section: Introductionmentioning

confidence: 99%

LPI Radar Signal Recognition Based on Feature Enhancement with Deep Metric Learning

Ren,

Quan,

Shen

et al. 2023

Electronics

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Low probability of intercept (LPI) radar signals are widely used in electronic countermeasures due to their low power and large bandwidth. However, they are susceptible to interference from noise, posing challenges for accurate identification. To address this issue, we propose an LPI radar signal recognition method based on feature enhancement with deep metric learning. Specifically, time-domain LPI signals are first transformed into time–frequency images via the Choi–Williams distribution. Then, we propose a feature enhancement network with attention-based dynamic feature extraction blocks to fully extract the fine-grained features in time–frequency images. Meanwhile, we introduce deep metric learning to reduce noise interference and enhance the time–frequency features. Finally, we construct an end-to-end classification network to achieve the signal recognition task. Experimental results demonstrate that our method obtains significantly higher recognition accuracy under a low signal-to-noise ratio compared with other baseline methods. When the signal-to-noise ratio is −10 dB, the successful recognition rate for twelve typical LPI signals reaches 94.38%.

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“…Some scholars describe the characteristics of one-dimensional original waveforms through feature calculation. For example, in [10], wavelet ridges and high-order statistics are used to extract signal features. In [11], radar signal classification is achieved through the autocorrelation function and the directed graph model.…”

Section: Introductionmentioning

confidence: 99%

LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion

et al. 2022

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The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB.

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Radar signal feature extraction based on wavelet ridge and high order spectral analysis

Cited by 7 publications

References 0 publications

Radar Signal Recognition Based on Dual-Channel Model With HOG Feature Extraction

Radar Signal Recognition Based on Dual-Channel Model With HOG Feature Extraction

LPI Radar Signal Recognition Based on Feature Enhancement with Deep Metric Learning

LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion

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