Spatial and temporal independent component analysis of micro-Doppler features
Spatial and temporal independent component analysis of micro-Doppler features
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DOI：
10.1109/radar.2005.1435849
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发表时间：
2005-05
期刊：
影响因子：
--
通讯作者：
Victor C. Chen
中科院分区：
文献类型：
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作者：
Victor C. Chen
微多普勒特征可以看作是运动物体的独特特征，为物体的分类、识别和识别提供附加信息。独立分量分析（ICA）可以将微多普勒特征分解成代表物体显著物理运动属性的独立基函数。为了研究微多普勒特征，我们使用了由旋转物体和翻滚物体的雷达回波信号模拟生成的数据集。本研究采用快速ICA算法将微多普勒特征分解为一组时空无关的分量。将独立分量的空间特征与相应的时间特征相结合，可以提高分类、识别和识别的性能。
Micro-Doppler features can be regarded as a unique signature of an object with movements and provide additional information for classification, recognition and identification of the object. Independent component analysis (ICA) can decompose micro-Doppler features into independent basis functions that represent salient physical movement attributes of the object. To study ICA of micro-Doppler features, we used a dataset generated by simulation of radar returned signals from rotating objects and tumbling objects. Fast ICA algorithm was used in our study to decompose micro-Doppler features into a set of spatial and temporal independent components. Spatial characteristics of the independent components combined with the corresponding temporal characteristics can be used to improve performance of classification, recognition and identification.