低信噪比条件下的信噪比估计算法

Signal-to-noise ratio estimation algorithm at low signal-to-noise

针对低信噪比条件下(<0 dB)经典信噪比估计算法估计精度不高的问题,在经典最大似然信噪比估计算法的基础上,提出基于二分法和基于梯度下降法的循环迭代搜索算法,利用循环迭代寻优的方法提高低信噪比条件下的参数估计精度,降低信噪比估计偏差,理论分析了梯度搜索算法的优势,仿真结果表明,在设定信噪比范围内,两种迭代搜索算法在低信噪比条件下均具有优于基础最大似然信噪比估计算法的估计性能,且梯度迭代搜索算法的收敛速度相对较快。

For problem that performance of classic SNR estimation algorithm is poor. Maximum likelihood amplitude estimation algorithm is improved in this paper. It used method of bisection and steepest descend iterative search method to improve the parameter estimation accuracy and reduces the SNR estimation bias. Simulation results show that within the scope of set SNR, two iterative search algorithm performance better than ML estimation algorithm, and steepest descend iterative search method with less number of iterations.