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Radar Emitter Signal Identification Based on Weighted Normalized Singular-value Decomposition
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摘要: 随着现代技术不断更新，雷达种类及相关技术得到不断发展，雷达辐射源信号的识别逐渐成为一个十分重要的研究领域。该文主要针对辐射源信号识别中的调制类型识别问题，从数据能量角度出发，在奇异值分解(Singular Value Decomposition, SVD)基础上进行优化，提出基于权重归一化奇异值分解特征提取算法。该文从奇异值分解的滤波效果、数据矩阵行数对分解结果的影响及不同分类模型识别效果等方面进行分析。实验结果表明该算法对常用雷达信号有较好滤波和识别效果，在–20 dB条件下滤波重构信号与原始信号余弦相似度值仍保持在0.94左右，在判别置信度
$\alpha $ 为0.65条件下识别正确率仍维持在97%以上。此外实验还表明相对于传统PCA算法，基于权重归一化奇异值分解特征提取算法拥有更好的鲁棒性。Abstract: With the continuous advancement of modern technology, more types of radar and related technologies are continuously being developed, and the identification of radar emitter signals has gradually become a very important research field. This paper focuses on the identification of modulation types in radar emitter signal identification. We propose a weighted normalized Singular-Value Decomposition (SVD) feature extraction algorithm, which is based on the perspective of data energy and SVD. The filtering effect of complex SVD is analyzed, as well as the influence of the number of rows of data matrix on the decomposition results, and the recognition effect of different classification models. The experimental results show that the algorithm has better filtering and recognition effects on common radar signals. Under –20 dB, the cosine similarity values of the reconstructed and original signals remain at about 0.94, and the recognition accuracy remains above 97% under a confidence level$\alpha $ of 0.65. In addition, experiments show that the weighted normalized SVD feature extraction algorithm has better robustness than the traditional Principal Component Analysis (PCA) algorithm. -
图 3 权重归一化奇异值特征提取示意图
Figure 3. Weighted normalized singular value feature extraction schematic
图 5 奇异值分解滤波重构信号及加噪信号与原始信号的欧式距离相似度对比示意图
Figure 5. Schematic diagram of the Euclidean distanceof the reconstructed signal and the noisy signal with the original signal with the signal-to-noise ratio
图 6 奇异值分解滤波重构信号及加噪信号与原始信号的余弦相似度随信噪比变化示意图
Figure 6. Schematic diagram of the cosine similarity of the singular value decomposition filter reconstructed signal and of the original signal with the signal-to-noise ratio
图 7 数据矩阵行数对奇异值分解重构信号与原始信号欧氏距离影响示意图
Figure 7. Schematic diagram of the influence of the number of rows of data matrix on the singular value decomposition reconstructed signal and the original signal Euclidean distance
图 8 数据矩阵行数对奇异值分解重构信号与原始信号余弦相似度影响示意图
Figure 8. Schematic diagram of the influence of the number of rows of data matrix on the singular value decomposition reconstructed signal and the cosine similarity of the original signal
图 9 辐射源信号识别模型的训练流程图
Figure 9. Radiation source identification model training flowchart
图 10 WN-SVD算法和PCA算法在不同分类模型识别结果随信噪比变化图
Figure 10. The change chart of recognition results based on different classification models and SNR
图 11 信号基于权重归一化奇异值分解特征提取方法在不同判别参数条件下准确率分布图
Figure 11. Signals based on weighted normalized singular value decomposition feature extraction method for accuracy rate distribution under different discriminant parameters
图 12 信号基于PCA特征提取方法在不同判别参数条件下准确率分布图
Figure 12. Signal accuracy distribution based on PCA feature extraction method under different discriminant parameters
图 13 判别参数
$\alpha $ =0.85时4类信号在不同信噪比条件下准确率分布图Figure 13. The accuracy rate distribution chart for four types of signals with different signal-to-noise ratio when discriminating parameter
$\alpha $ =0.85 -
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