基于SAR仿真图像的地面车辆非同源目标识别

充分的合成孔径雷达(synthetic aperture radar, SAR)模板数据是目标识别算法(尤其是基于深度学习的智能目标识别算法)获得优异识别性能的关键, 基于实际测量获取充分SAR数据是不现实的, 基于电磁散射建模的SAR仿真成为当前获取充分样本的一种有效途径。SAR仿真图像与实测图像为非同源数据, 由于SAR仿真的目标几何模型与实物之间差异、SAR仿真过程中的传感器模型与实际传感器性能之间差异、实物所处的背景环境与SAR仿真的环境之间差异、电磁建模方法本身误差等因素导致SAR仿真图像与实测图像存在差异, 会影响识别性能。针对这一问题, 首先采用一种基于高频渐近技术和离散射线追踪技术的SAR仿真方法获取地面车辆目标的SAR仿真图像, 再利用卷积神经网络方法、线性/非线性特征变换方法实现对MSTAR实测数据的非同源SAR目标识别性能对比分析。实验结果表明, 直接使用SAR仿真数据无法实现对实测SAR数据有效识别, 而线性/非线性特征变换可以改善非同源SAR目标识别性能, 一定程度上缓解由于SAR仿真数据与实测数据存在差异导致的识别性能差的问题。

Non-homologous target recognition of ground vehicles based on SAR simulation image

Sufficient synthetic aperture radar (SAR) template data is the key to achieve excellent recognition performance of target recognition algorithms (especially intelligence target recognition algorithms based on deep learning). It is unrealistic to obtain sufficient SAR data from the actual measurements, so SAR simulation based on electromagnetic scattering modeling has become an effective way to obtain sufficient samples. Simulated SAR image and measured SAR image are non-homologous data. Due to the fact that the target geometric model of SAR simulation is not inevitable consistent with the real object, the SAR sensor model in SAR simulation may be different from the actual sensor performance, the background environment of the object is also inevitably different from that of SAR simulation, and the error of electromagnetic modeling method itself, etc., difference is a inevitable existing between the simulated and measured SAR images, which will affect the recognition performance. To address this problem, this paper first adopts a SAR simulation method based on high frequency asymptotic technique and discrete ray tracing technique to obtain SAR simulation images of ground vehicle targets, and then uses the convolutional neural network (CNN) method and the linear/nonlinear feature transformation method to realize the comparative analysis of non-homologous SAR target identification performance of MSTAR measured data. The experimental results show that the direct use of SAR simulation data cannot achieve the ideal recognition performance of the measured SAR data, while the feature transformation based on linear/nonlinear can improve the identification performance of non-homologous SAR target recognition, and to some extent, alleviate the poor recognition performance caused by the difference between SAR simulation data and measured data.