Maximum-likelihood estimation of a class of chaotic signals
Maximum-likelihood estimation of a class of chaotic signals
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DOI：
10.1109/18.370091
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
H. Papadopoulos;G. Wornell
中科院分区：
文献类型：
--
作者：
H. Papadopoulos;G. Wornell
混沌序列对应于帐篷地图动力学在一系列工程应用中具有潜在的吸引力。在存在高斯白噪声的情况下，基于极大似然方法，导出了这类序列的信号滤波、预测和平滑的最优估计算法。所得到的算法是高度非线性的，但具有方便的递归实现，在计算和存储方面都很有效。性能评估也包括在内，并与相关的Cramer-Rao界限进行比较。>
The chaotic sequences corresponding to tent map dynamics are potentially attractive in a range of engineering applications. Optimal estimation algorithms for signal filtering, prediction, and smoothing in the presence of white Gaussian noise are derived for this class of sequences based on the method of maximum likelihood. The resulting algorithms are highly nonlinear but have convenient recursive implementations that are efficient both in terms of computation and storage. Performance evaluations are also included and compared with the associated Cramer-Rao bounds. >