基于线性动态模型的雷达高分辨距离像小样本目标识别方法

为了解决雷达高分辨距离像识别系统对训练样本需求量过大的问题，该文提出一种基于线性动态模型的小样本目标识别方法。首先分析了距离像频谱的统计特性，然后从其广义平稳性出发，使用线性动态模型对距离像频谱幅度建模，并用期望最大化算法估计模型参数。实测数据的实验结果表明:即使在很少的训练样本条件下，该方法仍能获得较高的正确识别率和良好的拒判性能。

Linear Dynamic Model Based Radar HRRP Target Recognition under Small Training Set Conditions

To relax the heavy requirement of training sample size in the radar High Resolution Range Profile(HRRP) target recognition,a linear dynamic model based recognition method is proposed.Firstly,the statistical characteristic of HRRP’s frequency spectrum is analyzed and considered to be a Wide Sense Stationary(WSS) process.Then a linear dynamic model is employed to model the amplitude of frequency spectrum and an Expectation Maximization(EM) algorithm is adopted to estimate the model parameters.Finally,experimental results based on measured data show that the proposed method can obtain satisfactory recognition accuracy and rejection performance even with a very few training samples.