基于动态RCS的典型飞机目标识别

发布时间：2018-05-10 12:17

本文选题：动态雷达散射截面 + 小波分析　；参考：《现代雷达》2017年01期

【摘要】：工程上常用静态雷达散射截面(RCS)统计特性进行目标识别,但其可分测度小,正确识别率较低。文中在精确获取目标动态RCS序列的基础上,提出了一种基于离散小波能量的特征提取方法,对典型飞机目标进行分类识别。首先,根据空气动力学和运动学方程设定五种典型飞机目标的飞行航迹并解算其实时飞行坐标,从而获取时变的雷达视线姿态角;其次,应用多层快速多极子电磁计算方法仿真各型目标的动态RCS数据;然后,再基于动态RCS序列,计算其位置、分布等统计特征,并进行小波分解和重构,提取各型目标的统计特征和小波能量特征;最后,采用基于距离的类间距离判据,比较两种特征量的分类识别效果。仿真计算结果表明:相对传统的统计特征,离散小波能量特征能完整地体现目标的特征,且可分性测度更大,识别效果更为理想。
[Abstract]:In engineering, static radar cross section cross section (RCSs) statistics are commonly used for target recognition, but its separable measure is small and the rate of correct recognition is low. Based on the accurate acquisition of target dynamic RCS sequences, a method of feature extraction based on discrete wavelet energy is proposed to classify and recognize typical aircraft targets. Firstly, according to the aerodynamics and kinematics equations, the flight paths of five typical aircraft targets are set up and their real-time flight coordinates are calculated to obtain the time-varying radar line-of-sight attitude angle. The multilayer fast multipole electromagnetic calculation method is used to simulate the dynamic RCS data of various targets, and then, based on the dynamic RCS sequence, the statistical features such as location and distribution are calculated, and the wavelet decomposition and reconstruction are carried out. The statistical features and wavelet energy features of each type of target are extracted. Finally, the classification and recognition effects of the two kinds of feature variables are compared by using the distance based inter-class distance criterion. The simulation results show that compared with the traditional statistical features, the energy features of discrete wavelet can fully reflect the characteristics of the target, and the separability measure is larger, and the recognition effect is more ideal.
【作者单位】：空军工程大学防空反导学院;
【基金】：国家自然科学基金资助项目(61372033) 航空基金资助项目(20130196005)
【分类号】：TN957.52

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