一种双基地嵌套MIMO雷达快速角度估计算法  被引量:1

A Fast Angle Estimation Algorithm for Bistatic Nested MIMO Radar

在线阅读下载全文

作  者:张小飞[1] 赖欣 李建峰[1] ZHANG Xiaofei;LAI Xin;LI Jianfeng(College of Electronic Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China)

机构地区:[1]南京航空航天大学电子信息工程学院,南京211100

出  处:《现代雷达》2022年第5期1-5,共5页Modern Radar

摘  要:针对双基地嵌套多输入多输出雷达现有算法复杂度较高的问题,文中提出了一种基于泰勒展开离散傅里叶变换(DFT)的快速角度估计算法。该算法首先将匹配滤波输出转化为等效虚拟阵列接收信号的DFT空间谱,随后通过谱峰搜索得到波离方向(DOD)和波达方向(DOA)的初始角度估计,然后通过相位旋转技术在角度初始估计值附近搜索得到DOD和DOA的二次角度估计,最后根据虚拟阵列等效方向矩阵的一阶泰勒级数展开式对角度二次估计值进行误差修正,计算得到修正后的DOD和DOA的精确角度估计。与子空间算法相比,该算法避免了协方差矩阵计算和特征值分解,不仅提高了估计性能,而且显著降低了计算复杂度。仿真结果验证了所提算法的有效性和优越性。Aiming at the problem of high complexity of existing algorithms in bistatic nested multiple input multiple output radar,a fast algorithm for angle estimation based on Taylor approximation discrete Fourier transform(DFT)is proposed.In this algorithm,the output matched filtering is firstly transformed to the DFT spatial spectrum of the signal received by the equivalent virtual array.Then the initial angle estimation of direction of departure(DOD)and direction of arrival(DOA)can be obtained by searching the peaks of DFT spectrum.Moreover,the fine estimation of DOD and DOA can be obtained by refine searching around the initial angle estimation with phase rotation technology.Finally,the fine angle estimation is revised to obtain the accurate DOD and DOA angle estimation from the first-order Taylor series expansion of equivalent direction matrix of virtual array.Compared with subspace-based algorithm,the proposed algorithm can avoid the calculation of covariance matrix and eigenvalue decomposition.Simulation results have verified the superiorities of the proposed algorithm in terms of computational complexity and estimation accuracy.

关 键 词:双基地多输入多输出雷达 嵌套阵列 波达角与波离角估计 快速傅里叶变换 

分 类 号:TN957.51[电子电信—信号与信息处理]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象