Wang Qing, Yang Hang, Chen Hua, Dong Yangyang, Wang Laihua
School of Electrical and Information Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, China.
Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2017 Jan 19;17(1):190. doi: 10.3390/s17010190.
In this paper, a new low-complexity method for two-dimensional (2D) direction-of-arrival (DOA) estimation is proposed. Based on a cross-correlation matrix formed from the L-shaped array, the proposed algorithm obtains the automatic pairing elevation and azimuth angles without eigendecomposition, which can avoid high computational cost. In addition, the cross-correlation matrix eliminates the effect of noise, which can achieve better DOA performance. Then, the theoretical error of the algorithm is analyzed and the Cramer-Rao bound (CRB) for the direction of arrival estimation is derived . Simulation results demonstrate that, at low signal-to-noise ratios (SNRs) and with a small number of snapshots, in contrast to Tayem's algorithm and Kikuchi's algorithm, the proposed algorithm achieves better DOA performance with lower complexity, while, for Gu's algorithm, the proposed algorithm has slightly inferior DOA performance but with significantly lower complexity.
本文提出了一种用于二维波达方向（DOA）估计的新的低复杂度方法。基于由L形阵列形成的互相关矩阵，该算法无需特征分解即可自动配对仰角和方位角，从而避免了高计算成本。此外，互相关矩阵消除了噪声的影响，可实现更好的DOA性能。然后，分析了该算法的理论误差，并推导了波达方向估计的克拉美罗界（CRB）。仿真结果表明，在低信噪比（SNR）和少量快照的情况下，与Tayem算法和Kikuchi算法相比，该算法以较低的复杂度实现了更好的DOA性能，而对于Gu算法，该算法的DOA性能略逊一筹，但复杂度显著降低。
