Prediction of sea clutter based on chaos theory with RBF and K-mean clustering

被引:0
|
作者
Su Xiaohong [1 ]
Suo Jidong [1 ]
机构
[1] Dalian Maritime Univ, Informat Engn Coll, Dalian, Peoples R China
来源
PROCEEDINGS OF 2006 CIE INTERNATIONAL CONFERENCE ON RADAR, VOLS 1 AND 2 | 2006年
关键词
sea clutter; chaos phase space reconstruction; C-C method; Cao method; RBF K-means;
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
Artificial Neural Network (ANN) has been widely applied in time series analysis, typically, it can give an effective method to solve complicated problems which are too complex to understand in physic and statistic method, or observation data varied statistically and the data generated in nonlinear mechanism. Based on the underlying dynamic mechanism of the sea clutter, to reconstruct the nonlinear model of dynamical phase space, correlation integral (also called C-C method) and Cao method are used to get time delay (tau) and embedding dimension (m) in this paper. Furthermore, an algorithm of Redial Basis Function (RBF) with k-mean clustering to adjust and modify the networks is also presented to predict the nonlinear characteristic sea clutter for the goal of detecting the weak target signals beneath the sea clutter. With the new algorithms, computation complexity can be deduced while its reliability can be greatly improved. It also can satisfy the real-time requirement in real application. More detailed calculates and test results are presented.
引用
收藏
页码:1675 / +
页数:2
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