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首页> 外文会议>2002 6th International Conference on Signal Processing Proceedings (ICSP'02) Vol.1; Aug 26-30, 2002; Beijing, China >SIGNAL PARAMETER ESTIMATION AND LOCALIZATION VIA MAXIMUM LIKELIHOOD USING A SENSOR ARRAY IN THE PRESENCE OF LEVY NOISE
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SIGNAL PARAMETER ESTIMATION AND LOCALIZATION VIA MAXIMUM LIKELIHOOD USING A SENSOR ARRAY IN THE PRESENCE OF LEVY NOISE

机译：在存在大噪声的情况下，使用传感器阵列通过最大似然来估计信号参数并进行本地化

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摘要

In this work we investigate an alternative to the stochastic Gaussian Maximum Likelihood (ML) method that deals with sub-Gaussian signals. The proposed system is one where the sources are stochastic and Gaussian, and the transfer medium is varying in a highly impulsive manner, introducing the sub-Gaussian nature at the receiver. Alternatively, the impulsive transformation to the signals can be viewed as part of the source model, creating a multivariate source signal whose components cannot be independent and is of impulsiveness equal to the one of the Cauchy distribution. The Levy α-stable distribution, of characteristic exponent 0.5 and index of symmetry 1, is used together with the multivariate Gaussian density to model the signal, and the resulting probability density function is derived. Based on this density, the stochastic ML estimator is formulated. A separable solution of the estimator is given, and simulations demonstrating the performance gains relative to the Gaussian-based ML estimator are provided.
机译：在这项工作中，我们研究了处理次高斯信号的随机高斯最大似然（ML）方法的替代方法。所提出的系统是其中源是随机的和高斯的，并且传输介质以高度脉冲的方式变化，从而在接收器处引入了亚高斯性质。或者，可以将对信号的脉冲变换视为源模型的一部分，从而创建一个多元源信号，该信号的分量不能独立并且具有与柯西分布之一相等的冲量。将特征指数为0.5且对称指数为1的Levyα稳定分布与多元高斯密度一起用于信号建模，并推导得出的概率密度函数。基于此密度，制定了随机ML估计量。给出了估计器的可分离解决方案，并提供了仿真，证明了相对于基于高斯的ML估计器的性能提升。

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