基于灰色关联和不确定推理的HRRP 目标识别

由于HRRP 固有的信息有损压缩,利用单帧HRRP 样本进行目标识别时, 存在经典算法识别率较低而改进后的算法一般复杂度较高的问题。为获得更加稳健、可信的识别效果,文中基于MYCIN 模型,引入灰色关联算子,构造了不确定因子,提出了一种基于HRRP 序列的雷达目标识别方法。基于五种飞机模型高分辨距离像数据的仿真实验表明:与单样本的识别算法相比,所提出的算法具有识别精度高、稳定性好、抗干扰能力强等优点,具有较好的工程应用前景。

A HRRP Target Recognition Method Based on Grey Incidence and Uncertainty Reasoning

Due to the inherent information lossy compression of HRRP( high-resolution range profile), the methods using signal frame HRRP have some defects that the recognition rate of classical methods is low and the complexity of improved algorithm is higher generally. To achieve steadier and more reliable recognition result, a radar target recognition algorithm is proposed based on HRRP''s sequence in the thesis. In this method, grey incidence operator is introduced into the probability-reasoning theory, and the calculation method of uncertain factor is given. Simulation results based on a HRRP dataset of five aircraft models demonstrate that compared with recognition algorithms with a single sample, the proposed algorithm has good prospects for engineering applications with higher recognition rate, better stability, and stronger anti-interference.