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Radar Target Modeling VIA Wavelets and Genetic Algorithm

机译:通过小波和遗传算法建模雷达目标

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A new modeling procedure, called the joint soft-thresholding maximum likelihood (JSTML) mthod, is proposed fo rradar scattering to vover the important practical case where the broadband scattering signal is received with a low signal-to-noise ratio (SNR). The basic idea underlying the JSTML method is first using a soft-thresholding prefiltering process to enhance SNR, and then finding the estimates of the parameters in GTD-based model throgh the maximum likelihood (ML) method. The gentic algorithm is used to over-come the issue of convergence to the local minimum. To reduce the computational load, our choice for an initial estimator is the matrix pencil method combined wiht GTD-based model, whcih provides initial estimates of all parameters including a parameterizing the frequency dependence of the scattering mechanism. he simulation results shwo that tha JSTML method has better noise performance than the ML method.
机译:提出了一种称为关节软阈值的最大似然(JSTML)Mthod的新建模程序(JSTML)MTHOD,对VOVER进行了重要的实际情况,其中接收具有低信噪比(SNR)的宽带散射信号。 JSTML方法底层的基本思想首先使用软阈值预选过程来增强SNR,然后找到基于GTD的模型Throgh中的参数的估计最大似然(ml)方法。滋养算法用于过度来到局部最小值的收敛问题。为了减少计算负载,我们对初始估计器的选择是基于GTD的模型组合的矩阵铅笔方法,提供了包括参数化散射机制的频率依赖性的所有参数的初始估计。他仿真结果SHWO Tha JSTML方法比ML方法具有更好的噪声性能。

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