EXACT MAXIMUM-LIKELIHOOD PARAMETER-ESTIMATION OF SUPERIMPOSED EXPONENTIAL SIGNALS IN NOISE
EXACT MAXIMUM-LIKELIHOOD PARAMETER-ESTIMATION OF SUPERIMPOSED EXPONENTIAL SIGNALS IN NOISE
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DOI：
10.1109/tassp.1986.1164949
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发表时间：
1986-10-01
期刊：
影响因子：
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通讯作者：
MACOVSKI, A
中科院分区：
文献类型：
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作者：
BRESLER, Y;MACOVSKI, A
本文提出了一个统一的框架，用于噪声中叠加指数信号参数的精确最大似然估计，包括时间序列和阵列问题。根据信号的线性预测多项式导出了ML准则的精确表达式，并给出了使该准则最大化的迭代算法。该算法同样适用于阵列问题中信号相干的情况。仿真结果表明，估计器能够提供更准确的频率估计比目前现有的技术。该算法是类似的Kumaresan等人独立得出的。除了它的实用价值，本配方是用来解释以前的方法，如Prony的，Pisarenko的，及其修改。
A unified framework for the exact maximum likelihood estimation of the parameters of superimposed exponential signals in noise, encompassing both the time series and the array problems, is presented. An exact expression for the ML criterion is derived in terms of the linear prediction polynomial of the signal, and an iterative algorithm for the maximization of this criterion is presented. The algorithm is equally applicable in the case of signal coherence in the array problem. Simulation shows the estimator to be capable of providing more accurate frequency estimates than currently existing techniques. The algorithm is similar to those independently derived by Kumaresan et al. In addition to its practical value, the present formulation is used to interpret previous methods such as Prony's, Pisarenko's, and modifications thereof.