OFDM中利用酉变换和结构最小二乘的角度和时延联合估计

摘要

在正交频分复用系统中,低信噪比条件下基于子空间分解的角度和时延联合估计算法的估计精度受限,为此提出一种利用酉变换和结构最小二乘的联合估计算法.利用酉变换将接收数据转换至实数域,然后用二维结构最小二乘建立目标优化函数,计算含有角度和时延信息的两个实对角矩阵.将这两个实矩阵构成一个复矩阵后对该复矩阵进行特征值分解,通过特征值实部和虚部的对应关系实现角度和时延的配对.该算法的目标函数合理地考虑了误差项之间的耦合关系,更加精确地修正了存在误差的信号子空间矩阵,因此估计结果更接近最优解.实验结果表明,该算法的估计精度和估计成功率比传统子空间算法高.%In an orthogonal frequency division multiplexing (OFDM) system,traditional subspace-based joint angle and delay estimation algorithms show significant performance degradation at low signal-to-noise ratio (SNR).To solve the problem,a new algorithm using unitary transform and structured least squares (SLS) is proposed.With unitary transform,data are transformed to the real number domain.Two-dimensional SLS is then used to estimate two real-valued diagonal matrices that contain information of angles and delays.A complex matrix is constructed with the two real-valued matrices,and eigenvalues of the complex matrix are calculated.The real and imaginary parts of the eigenvalues correspond to angles and delays,respectively.Since SLS takes into account the coupling relationship between noise terms and restores the estimated signal subspace matrix,its estimation performance is closer to optimum than those of the others.Simulation results show that the proposed USLS-JADE algorithm is superior to traditional subspace-based algorithms in terms of accuracy and success rate.