改进传播算子的角度和多普勒频率联合估计算法  

Joint Angle and Doppler Estimation by Improved Propagator Method

作　　者：郭利凯 吴瑛[1] 尹洁昕 

机构地区：[1]解放军信息工程大学信息系统工程学院,河南郑州450000

出　　处：《信号处理》2017年第5期741-748,共8页Journal of Signal Processing

基　　金：国家自然科学基金(61201381);信息系统工程学院优秀青年基金(2016603201)

摘　　要：在角度和多普勒频率的联合估计中,针对传统传播算子算法在低信噪比下精度差的问题,提出了一种改进传播算子算法。该算法首先利用时域平滑技术得到扩展流型矩阵,并使用传播算子矩阵的平移不变性获得角度的粗略估计;然后,构造与扩展流型矩阵具有正交关系的新传播算子矩阵,并以此建立2维优化问题的目标函数;最后,引入导向矢量的Frobenius范数约束,将2维优化问题转换为1维优化问题,通过1维局部搜索获得角度和多普勒的精确估计。本文算法不需要对协方差矩阵进行特征值分解,角度和多普勒估计结果可以自动配对。仿真实验表明,本文算法有较高的估计精度和分辨率。In joint angle and Doppler estimation, an improved propagator method is proposed to solve the problem that the accuracy of the traditional propagator method is poor at low signal-to-noise ratio （ SNR ）. Firstly, by applying temporal smoothing technique a new array steering matrix is constructed which has a double Vandermonde structure, and then the ro- tational invariance technique is utilized to obtain the coarse estimation of angles. Secondly, a new propagator matrix is con- structed which is orthogonal to the extended array steering matrix, and then a quadratic optimization function is formulated. Finally, with the use of the Frobenius norm constraint of the steering vector, the 2-dimensional optimization problem is de- composed into 1-dimensional optimization problem which can be solved by 1-dimensional local searching. Both eigenvalue decomposition （EVD） of the covariance matrix and pair matching are not required in the proposed algorithm. Simulation results show that the proposed algorithm exhibits superior accuracy and resolution ratio.

关 键 词：角度和多普勒联合估计 改进传播算子 Frobenius范数约束 降维算法 

分 类 号：TN911.7[电子电信—通信与信息系统]