具有递归和反馈的可编程典型相关分析器

Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers Pub Date : 1995-10-30 DOI:10.1109/ACSSC.1995.540570

M. Kahn, W. Gardner, M.A. Mow

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引用次数: 7

摘要

提出了一种改进的可编程典型相关分析仪(PCCA)，利用递归和反馈来改进盲自适应空间滤波。具体实现开发利用交替块幂方法与广义的Gram-Schmidt正交过程。为了利用信号的循环平稳性和恒模特性，本文开发了几种新型递归/反馈pcca的实现方法。根据输出信噪比和收敛性对所提出的技术的性能进行了经验评估和表征。

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Programmable canonical correlation analyzers with recursion and feedback

Modified programmable canonical correlation analyzers (PCCA) are developed to exploit recursion and feedback for improved blind adaptive spatial filtering. Specific implementations are developed utilizing an alternating block power method with a generalized Gram-Schmidt orthogonalization procedure. Several realization of these new recursive/feedback PCCAs are developed for exploitation of cyclostationarity and constant modulus signal properties. The performance of the proposed techniques is evaluated empirically and characterized in terms of output SINR and convergence behavior.

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Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers

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