摘要
针对小采样数据长度下,采样协方差矩阵对统计协方差矩阵估计不准,影响传统最大最小特征值(MME)检测算法检测性能的问题,提出一种基于逼近收缩(OAS)矩阵估计的改进MME检测算法。首先利用OAS估计量对采样数据做协方差矩阵估计,再对估计协方差矩阵特征值分解,将最大最小特征值之比作为检测统计量,克服了传统MME算法检测门限随采样点大幅波动的缺陷,提高了检测门限的鲁棒性。仿真结果表明,所提算法的检测门限具有鲁棒性,检测性能提高了1 d B^2 d B。
Aiming at the problem that the inaccurate estimation of sample covariance matrix for thestatistical covariance matrix could lead to poor detection performance of the MME detection algorithmwhile sampling data length is small,a spectrum sensing algorithm based on estimated covariance matrixMME detection is proposed. First,the OAS estimator is used to estimate the statistical covariancematrix of sampling data. Then,the eigenvalue decomposition for the estimated covariance matrix ismade. Finally,the ratio of maximum eigenvalue and minimum eigenvalue is taken as the detectionstatistic,which overcomed the defects that the detection threshold of the traditional MME algorithmfluctuate sharply with the sampling point incearcing,improved the robustness of the detection threshold.Simulation results show that the proposed algorithm has a robust detection threshold. Meanwhile,thedetection performance was improved by 1 dB^2 dB.
出处
《火力与指挥控制》
CSCD
北大核心
2016年第5期71-75,79,共6页
Fire Control & Command Control
关键词
认知无线电
频谱感知
最大最小特征值
协方差矩阵估计
随机矩阵理论
cognitive radio,spectrum sensing,Maximum-Minimum Eigenvalue（MME）,covariancematrix estimation,Random Matrix Theory（RMT）
