基于改进的半监督主动学习的雷达信号识别

为解决在雷达信号分类识别过程中训练样本较少的问题，本文提出了联合主动学习和半监督学习，并对其伪标记样本进行迭代验证改进的分类算法。针对复杂的电磁环境下雷达信号识别率低的问题，本文将径向高斯核时频分析应用于雷达信号，并对时频分布进行奇异值分解，提取出奇异向量作为雷达信号识别的特征参数。针对传统的半监督主动学习算法的不足，利用改进的半监督主动学习算法构建分类器，该算法通过对伪标记样本进行迭代验证来提高伪标记信息的准确性，从而改善了最终的分类性能，实现了在可获取的有标签样本数量较少的条件下对雷达信号的高概率识别。仿真结果表明，本文提出的特征识别方法可以获得较高的识别率。 

The improved semi-supervised active learning for radar signal recognition

In the process of radar signal recognition, fewer training samples is a common and challenging problem. A novel algorithm named improved semi-supervised active learning is proposed for signal classification, which is based on pseudo-labels verification procedure. For the problem of low radar signal recognition in complex electromagnetic environment, the time-frequency analysis of radially Gaussian kernel is applied to radar signals. Through singular value decomposition of time-frequency distribution, it extracts its singular values as feature parameters for radar signal recognition. In order to overcome the shortcomings of the traditional semi-supervised active learning algorithm, a classifier is constructed using an improved semi-supervised active learning algorithm. The proposed algorithm enables a collaborative labeling procedure by both human experts and classifiers to acquire more confidently labeled samples to improve the final classification performance and realize the high probability of radar signal recognition when the number of available labeled samples is small. Simulation results show that the proposed feature recognition method can achieve higher radar signal recognition at low SNR.