改进的子空间盲信噪比估计方法

基于子空间分解的信噪比估计方法广泛的适用于各类调制方式,但是存在低信噪比条件下信号子空间维度计算误差过大,从而导致估计性能下降的问题。通过对有限长度数据样本条件下子空间方法的分析和仿真,确定了信号子空间维度估计不准的原因,并提出一种新的适用于短数据的子空间信噪比估计方法,并分别针对不同的样本长度和信号调制方式进行实验验证。实验结果表明,低信噪比条件下,新方法能将估计误差降低了了0.3~2dB。

An Estimate Method of Blind SNR Applicable to Subspace of Short Data

Aimed at the problems in the SNR estimate method of the subspace that error is excessive in calculating signal subspace dimension and this makes the estimate precision low under conditions of low SNR, in this paper, a new robust SNR estimate method is proposed applicable to the subspace SNR estimate of small size data through the analysis, the simulation, and the determination of the subspace method under conditions of the finite sample date. The results show that the estimate error can be decreased by 0.3~2 dB by use of the new method in low SNR and short data samples environment