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Parameter estimation for clutter Weibull-distributed model based on improved MLE-NR method


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Parameter estimation for clutter Weibull-distributed model based on improved MLE-NR method
Hou ZhiCui CanZhang DuoWu Wen
Ministerial Key Laboratory of JGMT,NUST,Nanjing 210094,China
maximum likelihood estimation-Newton Raphson method Weibull-distributed model parameter estimation initial value Hessian matrix Monte-Carlo simulation
To solve the problem of the traditional maximum likelihood estimation-Newton Raphson(MLE-NR)method that the initial value must be selected carefully to ensure the convergence of iteration,an improved MLE-NR method for parameter estimation of clutter Weibull-distributed model is presented here.The Hessian matrix of the iteration point is calculated,the divergent points in iteration process and wrong initial iteration points are adjusted to a convergence region according to the determinant value of the Hessian matrix,so that the divergent iteration is convergent again and the model parameter can be estimated correctly.Monte-Carlo simulations are proceeded with N=500 and the lengths of the random sample data are 256,512,1 024,2 048,4 096 respectively.The results show that the improved MLE-NR method is convergent for sample data with different length.The Monte-Carlo simulation and processing results based on measured data demonstrate the effectiveness and robustness of this method.


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Last Update: 2014-12-31