基于微多普勒效应特征的空中目标识别

Air Target Recognition Based on Micro-Doppler Effect Features

特征提取是基于微动特征雷达目标识别的关键一环。传统方法提取的特征为线性、浅层的,导致表征微多普勒效应的能力有限。针对这些问题,采用非线性网络进行特征学习,建立了基于微多普勒效应的飞机目标识别深度网络。通过构建处理微多普勒效应的卷积神经网络(CNN)模型,从微多普勒频域数据中自动提取非线性深层次属性特征,实现空中目标分类识别。在实际测量的微多普勒频域数据上的大量实验结果表明,所提方法具有良好的目标识别性能和泛化性能。

Feature extraction is the key technique for radar target recognition based on micro-Doppler effect.The features extracted by traditional methods are linear and shallow,which results in the limited capability to characterize micro-Doppler effect.Aiming at these issues,this paper a-dopts nonlinear network for feature learning,builds up deep network for aircraft target recognition based on micro-Doppler effect.By constructing a convolutional neural network(CNN)model for dealing with micro-Doppler effect,the nonlinear deep property features of targets are fully extrac-ted from micro-Doppler data of frequency domain,so air target classification and recognition are re-alized.The extensive experimental results on the real measured micro-Doppler frequency domain data show that the proposed method achieves good target recognition performance and generaliza-tion performance.