针对小样本条件下的盲信噪比估计误差较大问题,结合信号子空间分解方法,提出一种基于有限样本信息准则( finite sample information criterion,FSIC)的盲信噪比估计算法,并推导出基于FSIC盲信噪比估计算法的最大似然形式.在小样本情况下,FSIC的引入克服了传统信息论方法产生的过拟合和欠拟合问题,降低计算复杂度.在不需要已知信号调制方式、载波频率、波特率等先验知识的前提下,能够在加性高斯白噪声信道(AWGN)和多径信道(Rayleigh)下对常用调制信号进行有效的信噪比估计.在信噪比-25 dB～25 dB范围内,其平均估计误差小于1 dB,表明该算法可有效应用于小样本盲信噪比估计.%Combined with a signal subspace decomposition approach, a new FSIC algorithm with finite sample information criterion was introduced to solve the blind SNR estimates with finite sample in non-cooperative commu-nication , and its likelihood form was also deduced. FSIC can avoid the underfit or overfit produced by the tradition-al information theory with finite samples, and it can also reduce the complexity. Under the condition of prior-knowl-edge of received signals unknown, such as modulation types, carrier frequency and baud rate, FSIC can effectively estimate the SNR for communal modulation signals in additional white Gaussian noise ( AWGN) channels and mul-tipath channels. The SNR mean estimation error of FSIC is less than 1 dB from -25 dB to 25dB. The results show that FSIC algorithm can be well applied the blind SNR estimate with finite sample in non-cooperative communica-tion.
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