山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (3): 8-14.doi: 10.6040/j.issn.1672-3961.0.2019.062
摘要:
针对飞机目标的自动识别问题,提出一种联合特征提取与分类的Chirplet神经网络方法,实现一维高分辨率距离像的识别。Chirplet神经网络将Chirplet原子变换用于多层前馈神经网络结构的输入层,替换传统的激励函数对距离像序列进行特征提取;网络的分类部分由隐层和输出层组成。在训练过程中调整神经网络权值的同时,完成对Chirplet原子时频参数的自动调整,协调优化特征参数和分类器参数,使Chirplet神经网络同时实现特征提取和目标分类。对4类飞机目标的仿真测试结果表明,相比时频变换和Gabor原子网络等方法,具有四特征参数的Chirplet神经网络方法具有较高的识别率和抗噪性能。
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