Object-oriented algorithm for range super-resolution estimation of LFMCW car sensors

被引：0
|
作者：
Li, Y [1 ]
Liyang, L [1 ]
Pan, WF [1 ]
Chen, YQ [1 ]
Feng, ZH [1 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, State Key Lab Microwave & Digital Commun, Beijing 100084, Peoples R China
来源：
2000 5TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS I-III | 2000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：
An object-oriented algorithm applicable to range super-resolution estimation of LFMCW car sensors is proposed. By utilizing prior information on target distribution pre-extracted by group segmentation, the complicated target estimation problem is reduced to a simple minimization problem. which is then solved by Hopfield neural network (HNN). Analysis shows that the prior information helps to decrease computation complexity and enhance resolution. Both simulation and experimental results have demonstrated the superiority of this algorithm over other superresolution algorithms such as MUSIC, ME(AR).
引用
收藏
页码：1810 / 1813
页数：4
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