基于高阶矩特征选择和权值优化的信噪比估计OA北大核心CSTPCD

As an important part of signal parameter estimation,signal-to-noise ratio(SNR)estimation can provide pri-or information for power control,modulation classification,channel estimation,and dynamic mode switching,etc.Recently,high-order moments(HOMs)based algorithms have been widely concerned due to the advantages of low computational complexity and high real-time property.However,the estimation performance of the HOMs-based algorithms is still con-strained in extremely low or high SNR regions.In this paper,a SNR estimation algorithm based on feature selection and lin-ear combination of HOMs is designed,according to the distribution characteristics of HOMs.Firstly,the HOMs are screened by analyzing the relationship between different moments and SNR values.Based on this,we resort to the linear combination of the selected HOMs to estimate SNR.And the weights of linear combination are calculated by designing an optimization problem.The simulation results show that the proposed SNR estimation scheme makes a tradeoff among the estimation performance of high and low SNR regions.Compared with the existing HOMs-based algorithms,the proposed al-gorithm has a more comprehensive performance in the range of-10 dB to 20dB.