首页> 外文OA文献 >Robust principal component analysis for micro-doppler based automatic target recognition
【2h】

Robust principal component analysis for micro-doppler based automatic target recognition

机译:基于微多普勒自动目标识别的鲁棒主成分分析

摘要

Dealing with real data it is likely that it will exhibit the presence of unexpected observations within the data which can affect the correct reduction of the representative features of a target signature. For the speciffc case of micro-Doppler based classiffcation this problem can appear in the feature selection stage. To address this problem the Robust PCA based on the Minimum Covariance Determinant (MCD) estimator is introduced. The proposed technique showed to improve the overall classiffcation accuracy.
机译:处理真实数据时,很可能会在数据中显示出意外的现象,这可能会影响目标签名代表特征的正确减少。对于基于微多普勒分类的特殊情况,此问题可能会出现在特征选择阶段。为了解决这个问题,引入了基于最小协方差决定子(MCD)估计器的鲁棒PCA。所提出的技术表明可以提高整体分类精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号