Abstract
In order to extract the richer feature information of ship targets from sea clutter,and address the high dimensional data problem,a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP)based on the maxi-mum margin criterion(MMC)is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP).Multi-scale fusion is introduced to capture the local and detailed information in small-scale features,and the global and contour information in large-scale features,offering help to extract the edge information from sea clutter and further improv-ing the target recognition accuracy.The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space.Experimental results on the measured radar data show that the proposed method can effec-tively extract the features of ship target from sea clutter,further reduce the feature dimensionality,and improve target recogni-tion performance.
基金项目
国家自然科学基金(6227125561871218)
中央高校基本科研业务费专项(3082019NC2019002)
Aeronautical Science Foundation(ASFC-201920007002)
Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements()
NETL
NSTL
万方数据