Interference Recognition Based on Singular Value Decomposition and Neural Network
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摘要: 无线通信中的抗干扰技术对通信的稳定性和安全性都具有重要意义，干扰识别作为抗干扰技术的重要环节一直是研究的热点。该文提出一种基于奇异值分解与神经网络的干扰识别方法，该方法只计算信号矩阵的奇异值即完成特征提取，与传统方法相比节省了多个谱特性的计算量。仿真结果表明：基于奇异值分解与神经网络的干扰识别方法与传统方法相比在干信比为0 dB左右的条件下识别准确率有10%～25%的提高。Abstract: The anti-interference technology in wireless communication is great significance to the stability and security of communication. As an important part of anti-interference technology, interference recognition is a research hotspot. An interference recognition method based on singular value decomposition and neural network is proposed. This method only calculates the singular value of the signal matrix as the feature. Compared with the traditional method, it saves the computational complexity of multiple spectral features. The simulation results show that the recognition accuracy based on singular value decomposition and neural network is 10%~25% higher than the traditional method under the condition of jamming-signal ratio at 0 dB.
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Key words:
- Jamming recognition /
- Neural network /
- Singular value decomposition
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