DocumentCode :
762555
Title :
A multiple-model prediction approach for sea clutter modeling
Author :
Xie, Nan ; Leung, Henry ; Chan, Hing
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Calgary, Alta., Canada
Volume :
41
Issue :
6
fYear :
2003
fDate :
6/1/2003 12:00:00 AM
Firstpage :
1491
Lastpage :
1502
Abstract :
Accurate modeling of sea clutter is an important problem in remote sensing and radar signal processing applications. Due to a recent discovery that sea clutter, the electromagnetic wave backscatter from a sea surface, is chaotic rather than purely random, computational intelligence techniques such as neural networks have been applied to develop new models for sea clutter. In this paper, we propose using the multiple neural network model approach to construct a predictive model for sea clutter. The motivation comes from the observation that the sea usually has some unpredictable motions that result in impulsive events such as sea spikes. Although a single nonlinear model could describe the Bragg scattering reasonably as shown in the literature, it is usually incapable of capturing sea spikes motions. Therefore, target detection performance might be degraded when such a clutter model is employed. Using a multiple radial basis function (RBF) net predictor, we found that a sea clutter signal with different underlying dynamics from sea spikes to normal motions can be modeled accurately. The multiple model (MM) approach automatically assigns different RBF predictors to model sea spikes and other mechanisms like Bragg scattering. The proposed multiple RBF neural network uses the expectation-maximization algorithm and multistep prediction for training, and hence it is suitable for real-time signal processing. Using real-life radar clutter data collected at the east coast of Canada, the proposed MM approach is shown to be effective in isolating and characterizing various components of sea clutter and, therefore, provides a promising model for clutter suppression in radar detection.
Keywords :
chaos; neural nets; ocean waves; oceanographic techniques; optimisation; prediction theory; radar clutter; radar signal processing; Bragg scattering; Canada east coast; EM wave backscatter; chaos; clutter suppression; expectation-maximization algorithm; multiple neural network model; multiple radial basis function net predictor; multiple-model prediction approach; multistep prediction; nonlinear prediction; predictive model; radar clutter data; radar detection; radar signal processing applications; real-time signal processing; remote sensing; sea clutter modeling; sea spikes; sea surface; target detection performance; Backscatter; Clutter; Electromagnetic scattering; Neural networks; Predictive models; Radar scattering; Radar signal processing; Remote sensing; Sea surface; Surface waves;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2003.811690
Filename :
1220258
Link To Document :
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