LPI Radar Signal Modulation Recognition with Feature Fusion Based on Time- frequency Transforms

Li, Shitong; Quan, Daying; Wang, Xiaofeng; Jin, Xiaoping

doi:10.1109/wcsp52459.2021.9613612

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…G y (x, y) = H(x, y + 1) − H(x, y − 1) (10) where H(x, y) is the grayscale of the image. The gradient magnitude and gradient direction at the pixel point (x, y) are shown in formulas (11) and (12).…”

Section: Gradient Calculationmentioning

confidence: 99%

“…In order to verify the performance of the proposed algorithm in this paper, we first compared it with several traditional methods (GLCM+SVM [12] and MSST+SVM [13]) and deep-learning-based methods (CWT+CNN [17], Alexnet+SVM [18], and LPI-net [19]). The experimental results in terms of the average recognition accuracy on 12 kinds of radar signals are shown in Figure 5.…”

Section: Performance Comparison and Analysismentioning

confidence: 99%

“…As the SNR decreased, the radar signals gradually became progressively obscured by noise, resulting in a decline in the quality of the time-frequency images. As shown in Figure 5, the GLCM [12] was susceptible to noise interference at a low SNR, which hindered effectively capturing some of the image texture features, directly impacting the accuracy of the image recognition. However, the HOG features used in this paper had good noise immunity and could better extract texture and edge features in the radar time-frequency images, even at low SNRs.…”

Section: Performance Comparison and Analysismentioning

confidence: 99%

“…Guo et al [11] proposed a radar signal recognition method for low probability of intercept (LPI) signals based on a stacked autoencoder combined with support vector machines (SVMs). Li et al [12] extracted textural features from the Choi-Williams distribution (CWD) and the ambiguity function using the gray-level co-occurrence matrix (GLCM), and then applied them to the SVM for recognition. Quan et al [13] proposed a radar signal recognition algorithm based on multiple simultaneous compression (MSST) time-frequency transform and histogram of orientation gradient (HOG) features.…”

Section: Introductionmentioning

confidence: 99%

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Radar Signal Recognition Based on Bagging SVM

Yu,

Qi,

Shen

et al. 2023

Electronics

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Radar signal recognition under low signal-to-noise ratio (SNR) conditions is a critical issue in modern electronic reconnaissance systems, which face significant challenges in recognition accuracy due to signal diversity. A novel method for radar signal detection based on the bagging support vector machine (SVM) is proposed in this paper.This method firstly utilizes the Choi–Williams distribution (CWD) and the smooth pseudo Wigner-Ville distribution (SPWVD) to obtain different time–frequency images of radar signals, which effectively leverages CWD’s strong time–frequency aggregation and SPWVD’s robust cross-interference resistance. Moreover, histograms of oriented gradient (HOG) features are extracted from time–frequency images to train multiple SVM classifiers by bootstrap sampling. Finally, the performance of each SVM classifier is aggregated using plurality voting to reduce the risk of model overfitting and improve recognition accuracy. We evaluate the effectiveness of the proposed method using 12 different types of radar signals. The experimental results demonstrate that its overall identification rate reaches around 79% at an SNR of −10 dB, and it improves the recognition rate by 5% compared with a single classifier.

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Section: Gradient Calculationmentioning

confidence: 99%

Section: Performance Comparison and Analysismentioning

confidence: 99%

Section: Performance Comparison and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radar Signal Recognition Based on Bagging SVM

Yu,

Qi,

Shen

et al. 2023

Electronics

Self Cite

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“…When the SNR is -12 dB, the recognition accuracy of the proposed method is higher than other three time-frequency transform combinations. Moreover, we also compare our model with two traditional machine learning models ( [22] Meng, [23] Li), and three CNN-based models [24][25][26]. The experimental results are shown in Fig.…”

Section: B Time-frequency Image Comparisonmentioning

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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