Parameter Estimation Using a Radial Basis Function Network for Synthetic Aperture Radars

被引：0
|
作者：
Chen, Tao [1 ]
Ding, Yongfei [1 ]
Pang, Ruifan [1 ]
Gong, Cheng [1 ]
Xu, Dinghai [1 ]
Zhang, Hengyang [2 ]
机构：
[1] Aviat Ind China AVIC, Key Lab Av Integrat, Shanghai, Peoples R China
[2] Airforce Engn Univ, Inst Nav & Informat, Xian, Peoples R China
来源：
2018 INTERNATIONAL CONFERENCE ON MICROWAVE AND MILLIMETER WAVE TECHNOLOGY (ICMMT2018) | 2018年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we consider a novel algorithm of Doppler parameter estimation for synthetic aperture radars (SAR). This algorithm takes advantages from artificial neural networks. By proper training, a Radial Basis Function (RBF) network may provide accurate performance. Frequency Domain data are used to generate the estimates. The parameters include Doppler centroid and Doppler frequency rate. Both computer simulation and experiment data demonstrate that a RBF network is powerful to SAR parameter estimation.
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页数：3
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