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Parameter-free structural modeling: a contribution to the solution of the separation of highly correlated AR-signals

机译:无参数的结构建模:有助于解决高度相关的AR信号分离问题

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This paper develops the concepts and properties of composite parameter structural (CPS) modeling, and shows how such properties can be exploited for the separation of very highly correlated autoregressive signals. A CPS model recently developed and used to represent a signal of a given structure (given order of an AR model) but of unknown, or partially unknown, parameters, is investigated. The main feature of the described CPS model is the utilization in its design of almost ideal null filters, resulting in low noise sensitivity. The performance of the proposed algorithms is analyzed using computer simulations.
机译:本文研究了复合参数结构(CPS)建模的概念和属性,并展示了如何利用这些属性来分离非常相关的自回归信号。最近研究了一种CPS模型,该模型用于表示给定结构(AR模型的给定顺序)但参数未知或部分未知的信号。所描述的CPS模型的主要特征是在其设计中利用了几乎理想的零滤波器,从而降低了噪声灵敏度。使用计算机仿真分析了所提出算法的性能。

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