数据采集与处理2011，Vol.26Issue(5)：609-614,6.
一种鲁棒的基于子空间分解的盲信噪比估计方法
Robust Blind SNR Estimation Method Based on Subspace Decomposition
摘要
Abstract
To solve the poor robustness problem of the signal and noise subspace dimension estimation in the subspace based algorithm, it uses the knowledge of oversampling rate and proposes a new method to built the autocorrelation matrix, which reduces the relevance of data in the matrix,thus improving the precision of the estimation. For the fact that the minimum distance length (MDL) criteria can only accurately estimates the dimension in limited signal-to-noise ratio (SNR) scope, a noise power method is introduced to estimate the signal subspace dimension, which improves the performance of the original algorithm when the SNR is too low or too high, so that the SNR bound of the estimation is augmented. Simulation results show that the new method can directly deal with the intermediate frequency (IF) signal with a better estimation performance and it is not sensitive to the shaping filter roll-off factor and modulation mode.关键词
子空间分解/盲信噪比估计/自相关矩阵/噪声功率Key words
subspace decomposition/blind signal-to-noise ratio (SNR) estimation/autocorrelation matrix/noise power分类
信息技术与安全科学引用本文复制引用
张金成,彭华..一种鲁棒的基于子空间分解的盲信噪比估计方法[J].数据采集与处理,2011,26(5):609-614,6.基金项目
国家自然科学基金(61072046)资助项目. （61072046）
