基于GSVD的分布式MIMO雷达测向算法
GSVD-based distributed MIMO radar direction finding algorithm
张颢月 1师俊朋 1史姝赟 1吴奇龙2
作者信息
- 1. 国防科技大学电子对抗学院,安徽合肥 230037
- 2. 安徽省军区,安徽合肥 230001
- 折叠
摘要
针对分布式多输入多输出(multi-input multi-output,MIMO)雷达测向中存在的数据信息提取不充分、运算量偏大等问题,开展了基于广义奇异值分解(generalized singular val-ue decomposition,GSVD)的测向算法研究,以提高低信噪比条件下的角度估计性能.首先,建立了分布式阵列MIMO雷达回波信号的统一化表征模型;其次,将分布式MIMO雷达系统接收阵列数据的多线程GSVD问题转换为一个联合优化问题,运用交替最小二乘(alternating least squares,ALS)技术实现阵列信号流行矩阵的拟合,并引入子空间类算法实现目标角度联合估计;最后,对优化问题增加l1范数约束,避免了每次迭代中进行的奇异值分解运算,降低了算法运算量.仿真实验从角度联合估计、均方误差、运算时间等方面验证了所提算法的有效性.
Abstract
In order to solve insufficient data information extraction and large amount of opera-tions in distributed multi-input multi-output(MIMO)radar direction-finding,this paper stud-ied the direction-finding algorithm based on generalized singular value decomposition(GSVD),so as to improve the performance of target angle estimation under low signal-to-noise ratio(SNR).Firstly,the distributed MIMO radar echo signal model was established.Then,the multilinear GSVD problem of the receiving array data was converted into an opti-mization problem and alternating least squares(ALS)algorithm was applied to solve it,achie-ving the fitting of channel matrix.Besides,the subspace algorithm was introduced to realize the joint estimation of the target angles.Finally,the l1 constraint was provided to avoid the singular value decomposition operation in each iteration and the computational complexity was reduced.Simulation experiments have demonstrated that the effectiveness of the proposed al-gorithm in terms of joint angle estimation,mean square error and operation time.
关键词
分布式MIMO雷达/广义奇异值分解/阵列测向/交替最小二乘Key words
distributed MIMO radar/GSVD/array direction-finding/ALS引用本文复制引用
基金项目
国家自然科学基金资助项目(62071476)
湖南省科技创新计划项目(2021RC3080)
国防科技大学学校科研计划项目(ZK20-33)
国防科技大学自主创新基金项目(23-ZZCX-JDZ-45)
出版年
2024
NETL
NSTL
维普
万方数据