摘要
为解决目前声目标识别面临的鲁棒性不足问题,提出将流形学习应用到声目标的特征提取中,在经典流形学习算法的基础上,研究讨论了目标声信号频域中存在的低维流形,通过两种实际的地面和低空飞行声目标数据集进行对比识别实验,分析了基于流形学习的声目标特征提取方法的性能,结果表明基于流形学习的特征提取方法可以发现声信号的本质特征,提高了声目标识别系统的准确性和鲁棒性.
In order to overcome the deficiency of robustness of low altitude passive acoustic target recognition, the manifold learning is applied to the feature extraction of acoustic targets. Based on the classical algorithm of manifold learning, in the paper we study and discuss the low-dimensional manifold in the frequency-domain of acoustic signals. This method is used to solve the target recognition problem with two data sets to verify its effectiveness, after which the performance is analyzed. The result indicates that the manifold learning can discover the intrinsic feature and increase the accuracy and the robustness of low altitude passive acoustic target recognition system.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2011年第7期437-443,共7页
Acta Physica Sinica
基金
国家自然科学基金(批准号:60872113)资助的课题~~
关键词
声目标识别
特征提取
流形学习
acoustic targets recognition, feature extraction, manifold learning
