Low-Complexity MUSIC-Like Algorithm with Sparse Array
Low-Complexity MUSIC-Like Algorithm with Sparse Array
复制标题
具有稀疏数组的低复杂度类音乐算法
DOI：
10.1007/s11277-015-2987-9
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发表时间：
2015-08
影响因子：
2.2
通讯作者：
Jiang Qingping
中科院分区：
文献类型：
--
作者：
Liu Sheng;Yang Lisheng;Chen Zhixiang;Jiang Qingping
本文提出了一种低复杂度的稀疏线性阵列类音乐算法。三个均匀线性阵列组合成一个稀疏线性阵列。通过对阵列接收数据的四阶累积量进行组织，得到扩展的信号子空间。在此过程中，不需要实施特征值分解（EVD）或奇异值分解。然后，提出了一种类似音乐的方法来估计事件信号的到达方向。为了进一步降低计算复杂度，使用类似ESPRIT的算法来获得方向角的初始估计，从而可以显着缩小搜索范围。与经典的MUSIC和PM相比，所提出的类MUSIC算法表现出更好的角分辨率和更高的估计精度。此外，由于避免了EVD并减少了搜索范围，所提出的类MUSIC算法通过类ESPRIT算法进行预估计的计算负担很小。通过数值模拟证明了所提出方法的性能。
This paper presents a low-complexity MUSIC-like algorithm with sparse linear array. Three uniform linear arrays are combined into a sparse linear array. An extended signal subspace is got by organizing the forth-order-cumulant of array received data. During this process, no eigen-value decomposition (EVD) or singular-value decomposition needs to be implemented. Then, a MUSIC-like method is proposed to estimate the direction-of-arrival of incident signals. In order to bring down the computational complexity further, an ESPRIT-like algorithm is used to obtain the initial estimations of direction angles, by which the search range can be diminished significantly. Compared with the classical MUSIC and PM, the proposed MUSIC-like algorithm shows better angular resolution and higher estimation accuracy. Moreover, because of the avoidance of EVD and the reduction of search range, the computational burden of the proposed MUSIC-like algorithm with per-estimation by ESPRIT-like algorithm is small. The performance of the proposed method is demonstrated through numerical simulations.
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DOI：
--
发表时间：
2013-07
期刊：
International Journal of Engineering Research and
影响因子：
--
作者：
Karan Sharma;Sandeep Santosh
通讯作者：
Karan Sharma;Sandeep Santosh
DOI：
10.1109/29.17564
发表时间：
1989-05-01
期刊：
IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING
影响因子：
--
作者：
STOICA, P;NEHORAI, A
通讯作者：
NEHORAI, A
影响因子：
5.4
作者：
Yang, Zai;Xie, Lihua;Zhang, Cishen
通讯作者：
Zhang, Cishen
影响因子：
4.4
作者：
Yi Zhang;Z. Ye;Xu Xu-Xu;Nan Hu
通讯作者：
Yi Zhang;Z. Ye;Xu Xu-Xu;Nan Hu
影响因子：
5.4
作者：
Chen, Chiao En;Lorenzelli, Flavio;Yao, Kung
通讯作者：
Yao, Kung