基于标签重构的弹载毫米波距离像识别算法

毫米波引信通过发射宽带信号获得目标的精细结构信息，然而在非合作目标的探测与识别过程中，由于缺乏目标的类别信息，大量的目标样本无法得到充分的利用。针对这一问题，将基于拉普拉斯得分(LS)的监督特征选择算法推广到半监督情况，得到基于标签重构的拉普拉斯得分算法(LRLS)，并应用到非合作目标的识别中。LRLS的理论框架与LS相同，并利用标签重构技术获得半监督情况下的图拉普拉斯矩阵。为了更好地描述高维目标样本的相似性，在标签重构的过程中使用测地距离代替欧氏距离。实验结果表明，相对于传统的特征选择算法，LRLS能够得到更好的识别效果。

Label reconstruction based missile-borne MMW HRRP recognition algorithm

Millimeter wave fuze obtains the detailed structure information by transmitting a wideband signal. However, in the process of non-cooperative detection and recognition, a large number of target samples could not be fully utilized due to the lack of target class information. To this end, Laplacian Score(LS) is extended to Label Reconstruction based Laplacian Score(LRLS) and applied to the case of semi-supervised learning. Under the framework of LS, LRLE utilizes the label reconstruction technique to calculate the Laplacian matrix. In order to better describe the similarity between the high-dimensional samples, the Euclidean distance is replaced with the geodesic distance. The experimental results show that LRLS performs better than the traditional methods.