Data-adaptive training selection for radar applications

被引：0
|
作者：
Conte, E [1 ]
De Maio, A [1 ]
Farina, A [1 ]
Foglia, G [1 ]
机构：
[1] Univ Naples Federico II, DIET, I-80125 Naples, Italy
来源：
Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology | 2004年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we devise and assess two procedures for selecting secondary data in a very heterogeneous radar scenario including the presence of outliers. The new algorithms are based on the theory of the Generalized Likelihood Function, either one-step or two-step, and achieve an effective rejection of the outliers. We also present performance curves showing that, for some operational scenarios with more than one outliers, the new procedures can outperform some previously proposed data selection algorithms.
引用
收藏
页码：179 / 182
页数：4
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