基于有限长样本数据的信噪比估计

从信号模型上给出了信号子空间是有限维数的说明,并通过仿真说明信号与噪声子空间的特征值在短数据下无法准确获得。提出基于仅由噪声特征值构成的极大似然函数的改进AIC准则,实现了信号子空间的维数的准确估计,从而获得了一种在相对较小样本下更稳健的信噪比估计方法。从不同的信噪比大小与样本数据长度进行仿真验证,结果表明,该方法比原始的子空间方法能适应更低的信噪比和更短的数据样本长度。

SNR Estimation Based on Finite Sample Data

This paper first demonstrats that signal subspace has finite dimensions from signal model. A conclusion is obtained by simulation test that both eigenvalues of signal subspace and noise subspace can't be evaluated precisely due to small size data. Then signal subspace dimensions are estimated accurately with Modified-AIC criterion using likelihood function depending only on noise space eigenvalues. A new robust SNR estimation method is proposed with the application of modified-AIC criterion. Regarding a variety of SNR and data size, simulation results show that the new method can be applied in lower SNR and shorter data samples environment.