Estimation of the RiIG-Distribution Parameters Using the Artificial Neural Networks

被引:0
作者
Mezache, Amar [1 ]
Chalabi, Izzeddine [2 ]
机构
[1] Univ Constantine 1, Lab Signaux & Syst Commun, Dept Elect, Constantine 25010, Algeria
[2] Univ Msila, Fac Technol, Dept Elect, Msila 28000, Algeria
来源
2013 IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING APPLICATIONS (IEEE ICSIPA 2013) | 2013年
关键词
K-DISTRIBUTION; CLUTTER; NOISE;
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 order to improve the estimation of the RiIG (Rician Inverse Gaussian) model parameters, the authors attempt to achieve the parameter estimates using the inverse function of the RiIG CDF (Cumulative Distributed Function) which the latter can not be obtained in a closed form. However, the ANN (Artificial Neural Network) technique is preferred which has the ability to approximate this nonlinear complexity. From recorded sea-clutter data, the regressions of the real CDF are used at the input layer of the ANN estimator. The weights of the network are optimized in real time by means of the genetic algorithm (GA) tool. The mean square error of estimates (MSE) criterion is considered to assess the estimation performance. For almost cases, the experimental results show that adopting the proposed scheme of the ANN estimator turns out the best parameter estimates and also allows a better matching of real CDF and real PDF (Probability density Function) than the standard IMLM (Iterative Maximum Likelihood Method) estimator.
引用
收藏
页码:291 / 296
页数:6
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