【摘要】 为解决调制识别前端的信噪比估计问题,首先给出了一种新的基于子空间分解的盲信噪比估计算法,新算法通过利用信号的过采样率信息构造特定维数的自相关矩阵,避免了原算法中对信号和噪声空间维数的估计。同时为了降低算法计算复杂度,以及更好地跟踪信噪比的变化,给出了另一种基于PASTd的子空间跟踪算法来进行信噪比的跟踪估计。仿真结果表明,与经典的子空间分解算法相比,改进算法在性能上有着显著的优势;同时基于PASTd的信噪比估计算法相比基于子空间分解的估计算法更好地跟踪了信噪比的时变。更多还原

【Abstract】 A new blind SNR (signal to noise rate) estimation algorithm based on subspace decomposition was proposed. In this algorithm, an autocorrelation matrix of specific dimension was built by using the knowledge of oversampling rate, which can avoid the step of estimating the signal and noise subspace’s dimension in the original subspace based algorithm. A PASTd-based SNR estimation algorithm was proposed to decrease the computational complexity, and to better track the time-varying SNR. Simulation results show that the improved subspace based algorithm has evidently better performance than the typical one, and the PASTd based SNR estimation algorithm has better tracking performance than the subspace based method in time-varying SNR environment.更多还原