| 联合互协方差矩阵的快速波达方向估计 |
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| 引用本文： | 闫锋刚,荣加加,刘帅,沈毅,金铭. 联合互协方差矩阵的快速波达方向估计[J]. 系统工程与电子技术, 2018, 40(4): 733-738. DOI: 10.3969/j.issn.1001-506X.2018.04.03 |
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| 作者姓名： | 闫锋刚 荣加加 刘帅 沈毅 金铭 |
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| 作者单位： | 1. 哈尔滨工业大学(威海)信息与电气工程学院, 山东 威海 264209;2. 哈尔滨工业大学电子与信息工程学院, 黑龙江 哈尔滨 150001 |
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| 摘 要： | 针对常规子空间类波达方向(direction-of-arrival, DOA)估计中存在的子空间分解计算量过大问题,提出了基于均匀线阵的联合互协方差矩阵(joint cross covariance matrix,JCCM)DOA估计算法。基于阵列划分和矩阵重构思想,将均匀线阵划分成两个子阵,在求得这两个子阵接收数据互协方差矩阵后,重构一个新的矩阵即JCCM,利用JCCM的部分数据进行线性运算即可得到等价的信号子空间,然后构造多项式并求根，最终实现波达角估计。理论分析和仿真实验证明,算法避开了对协方差矩阵的特征值分解运算,在保证估计精度可接受的同时,有效降低了计算量,取得了更高的估计速度。
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Joint cross covariance matrix based fast direction of arrival estimation |
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| YAN Fenggang,RONG Jiajia,LIU Shuai,SHEN Yi,JIN Ming. Joint cross covariance matrix based fast direction of arrival estimation[J]. System Engineering and Electronics, 2018, 40(4): 733-738. DOI: 10.3969/j.issn.1001-506X.2018.04.03 |
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| Authors: | YAN Fenggang RONG Jiajia LIU Shuai SHEN Yi JIN Ming |
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| Affiliation: | 1. School of Information and Electrical Engineering, Harbin Institute of Technology (Weihai), Weihai 264209, China;;2. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China |
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| Abstract: | For the sake of reducing computation complexity caused by the subspace decomposition step in most of the state of the art subspace based algorithms for direction-of-arrival (DOA) estimation, a joint cross covariance matrix (JCCM) based algorithm is proposed using a uniform linear array (ULA). Based on the ideas of array division and matrix remodeling, the ULA is divided into two sub arrays, and two cross covariance matrices are computed by the received datum of the two sub arrays, with which a JCCM matrix is further reconstructed. Combing those three matrices, an equivalent signal subspace is found by conducting some low complexity linear operations, and a polynomial is finally constructed to estimate source DOA without subspace decomposition. Theoretical analysis and simulation results show that the proposed technique can provide acceptable performances with the computational complexity effectively reduced and the faster estimation speed achieved. |
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