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| Citation: | YUAN Ba, YAO Ping, and ZHENG Tianyao. Radar emitter signal identification based on weighted normalized singular-value decomposition[J]. Journal of Radars, 2019, 8(1): 44–53. doi: 10.12000/JR18053
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Radar Emitter Signal Identification Based on Weighted Normalized Singular-value Decomposition
DOI: 10.12000/JR18053
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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. -
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References
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- Figure 1. Singular value decomposition schematic
- Figure 2. Singular value feature extraction schematic
- Figure 3. Weighted normalized singular value feature extraction schematic
- Figure 4.
- 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
- 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
- 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
- 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
- Figure 9. Radiation source identification model training flowchart
- Figure 10. The change chart of recognition results based on different classification models and SNR
- Figure 11. Signals based on weighted normalized singular value decomposition feature extraction method for accuracy rate distribution under different discriminant parameters
- Figure 12. Signal accuracy distribution based on PCA feature extraction method under different discriminant parameters
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Figure 13. The accuracy rate distribution chart for four types of signals with different signal-to-noise ratio when discriminating parameter
$\alpha $ -
Figure 14. The accuracy distribution chart for four signals with different signal-to-noise ratio (–40～–15 dB) when discriminating parameter
$\alpha $