基于FLAKNN的雷达一维距离像目标识别

由于传统KNN算法在应用于高分辨一维距离像进行目标识别时，存在全局使用固定k值和未考虑各特征分量对分类的影响等不足，使得目标识别性能较差.提出一种改进的KNN算法:FLAKNN.通过提取目标高分辨率一维距离像的尺寸、熵、中心距、不规则度、去尺度特征、对称度等稳定特征，使用Fisher判别分析将所有特征分量投影至低维空间，使不同类别间具备最大可分性；结合相邻样本局部的分布情况和k取值的调整，最终使用少数服从多数的投票原则决定测试样本的类别.结果表明，相对传统KNN算法，该算法进一步提升了识别性能. 

Radar Range Profile Target Recognition Based on FLAKNN

Due to the deficiency of traditional KNN algorithm in target recognition of high range profile, such as using fixed k value globally and not considering the influence of each characteristic component on classification, the target recognition performance is poor. Therefore, an improved KNN algorithm-FLAKNN, was proposed. By extracting the stable characteristics such as the size, entropy, center distance, irregularity, scaling feature and symmetry of the high range profile of the target, Fisher discriminant analysis was used to project all feature components to the low-dimensional space, so as to achieve the maximum separability among different categories. Combined with the local distribution of adjacent samples and the adjustment of k value, the principle of majority voting was finally used to determine the category of test samples. The results show that compared with the traditional KNN algorithm, this algorithm further improves the recognition performance.