Adaptive Recognition Method for Unknown Interference Based on Hilbert Signal Space
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摘要: 针对大样本下未知干扰类型的分类识别问题，该文提出一种基于信号特征空间的未知干扰自适应识别方法。首先，基于Hilbert信号空间理论对干扰信号进行处理，建立干扰信号特征空间，进而利用投影定理对未知干扰进行最佳逼近，提出基于信号特征空间的概率神经网络(PNN)分类算法，并设计了未知干扰分类识别器的处理流程。仿真结果表明，与两种传统方法相比，该方法在已知干扰的分类精度方面分别提高了12.2%和2.8%；满足条件的未知干扰最佳逼近效果随功率强度呈线性变化，设计的分类识别器在满足最佳逼近的各类干扰中总体识别率达到91.27%，处理干扰识别的速度明显改善；在信噪比达到4 dB时，对未知干扰识别准确率达到92%以上。
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关键词:
- 无人机通信 /
- 未知干扰 /
- 自适应识别 /
- Hilbert信号空间 /
- 概率神经网络
Abstract: In order to solve the problem of classification and recognition of unknown interference types under large samples, an adaptive recognition method for unknown interference based on signal feature space is proposed. Firstly, the interference signal is processed and the interference signal feature space is established with the Hilbert signal space theory. Then the projection theorem is used to approximate the unknown interference. The classification algorithm based on signal feature space with Probabilistic Neural Network (PNN) is proposed, and the processing flow of unknown interference classifier is designed. The simulation results show that compared with two kinds of traditional methods, the proposed method improves the classification accuracy of the known interference by 12.2% and 2.8% respectively. The optimal approximation effect of the unknown interference varies linearly with the power intensity in the condition, and the overall recognition rate of the designed classifier reaches 91.27% in the various types of interference satisfying the optimal approximation, and the speed of processing interference recognition is improved significantly. When the signal-to-noise ratio reaches 4 dB, the accuracy of unknown interference recognition is more than 92%. -
表 1 干扰信号参数
干扰类型 干扰参数 数值 单音干扰 干扰频点(MHz) 150 多音干扰 干扰频点(MHz) 50, 100, ···, 250 部分频带干扰 覆盖带宽(MHz) 250～400 脉冲干扰 占空比(%) 10 线性调频干扰
(单分量)初始频率(MHz) 150 调频率 500 梳状谱干扰 分量数目 3 带宽(MHz) 800 
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表 2 干扰分类算法识别率比较
信号空间数据集 识别率(%) 单音干扰 多音干扰 部分频带 线性调频 脉冲干扰 梳状谱 总体识别率 传统SVM分类器 85.0 98.7 100 82.5 90 81.2 86.3 文献[12] 100 98.7 100 98.7 100 98.7 95.7 本文算法 100 100 100 91.0 100 100 98.5
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表 3 多分类算法性能比较(%)
干扰空间数据集 分类识别率 训练识别率 测试识别率 本文算法 传统算法 本文算法 传统算法 本文算法 传统算法 单音干扰 92.41 47.55 92.59 50.85 91.27 46.24 多音干扰 98.73 45.37 部分频带 81.01 45.99 线性调频 100 45.37 脉冲干扰 74.36 45.86 梳状谱 98.72 46.94 未知干扰 93.59 46.62
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