Electronic Countermeasures College, National University of Defense Technology, Hefei 230037, China.
Sensors (Basel). 2018 Oct 19;18(10):3553. doi: 10.3390/s18103553.
To increase the number of estimable signal sources, two-parallel nested arrays are proposed, which consist of two subarrays with sensors, and can estimate the two-dimensional (2-D) direction of arrival (DOA) of signal sources. To solve the problem of direction finding with two-parallel nested arrays, a 2-D DOA estimation algorithm based on sparse Bayesian estimation is proposed. Through a vectorization matrix, smoothing reconstruction matrix and singular value decomposition (SVD), the algorithm reduces the size of the sparse dictionary and data noise. A sparse Bayesian learning algorithm is used to estimate one dimension angle. By a joint covariance matrix, another dimension angle is estimated, and the estimated angles from two dimensions can be automatically paired. The simulation results show that the number of DOA signals that can be estimated by the proposed two-parallel nested arrays is much larger than the number of sensors. The proposed two-dimensional DOA estimation algorithm has excellent estimation performance.
为了增加可估计信号源的数量，提出了两平行嵌套阵列，它由两个带有传感器的子阵组成，可以估计信号源的二维（2-D）到达方向（DOA）。为了解决两平行嵌套阵列的测向问题，提出了一种基于稀疏贝叶斯估计的 2-D DOA 估计算法。通过一个向量化矩阵、平滑重建矩阵和奇异值分解（SVD），该算法减少了稀疏字典和数据噪声的大小。稀疏贝叶斯学习算法用于估计一维角度。通过联合协方差矩阵，估计另一个维度的角度，并且可以自动对两个维度的估计角度进行配对。仿真结果表明，所提出的两平行嵌套阵列可估计的 DOA 信号数量远远大于传感器的数量。所提出的二维 DOA 估计算法具有优异的估计性能。
