中文摘要：

提出一种基于一维搜索和距离函数的快速独立分量分析（FastICA）特征提取改进算法．该算法针对Fast ICA中迭代初始值的选取影响其收敛性的问题，通过一维搜索策略使其收敛性不依赖于初始值的选取．与此同时，根据红外图像的特性设计类内类间距离函数准则对提取的独立分量进行优化选择，保留对目标识别贡献大的独立分量特征，从而克服在高维特征子空间下随着训练图像样本数的增多，红外目标识别率和稳定性下降的问题．实测数据实验结果表明，与传统算法相比，该算法能够在提取少量红外目标特征的情况下达到更低的错分率，且算法在不同类别数下的错分率具有较强的鲁棒性．

英文摘要：

An improved fast independent component analysis based on one dimension search and distance function. initial value influences the convergence in fast ICA convergence result by using one dimension search. （ICA） feature extraction algorithm is proposed Aiming at the problem that the selection of the algorithm, the improved algorithm ensures the good Meanwhile, a rule based on distance function is designed to select the optimal features for the recognition according to the characteristics of the infrared image. Thus, the problem of the recognition rate and the robustness decreasing with the increasing number of training image samples is resolved. Compared with the traditional methods, the experimental results of real infrared images show that the proposed algorithm reaches a lower error classification rate with fewer infrared object features, and the error classification rate of the proposed method is robust in different kinds of classes.