Qin Yanhua, Liu Yumin, Liu Jianyi, Yu Zhongyuan
Institute of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2018 Jan 16;18(1):253. doi: 10.3390/s18010253.
Sparse Bayesian learning (SBL) is applied to the coprime array for underdetermined wideband direction of arrival (DOA) estimation. Using the augmented covariance matrix, the coprime array can achieve a higher number of degrees of freedom (DOFs) to resolve more sources than the number of physical sensors. The sparse-based DOA estimation can deteriorate the detection and estimation performance because the sources may be off the search grid no matter how fine the grid is. This dictionary mismatch problem can be well resolved by the SBL using fixed point updates. The SBL can automatically choose sparsity and approximately resolve the non-convex optimizaton problem. Numerical simulations are conducted to validate the effectiveness of the underdetermined wideband DOA estimation via SBL based on coprime array. It is clear that SBL can obtain good performance in detection and estimation compared to least absolute shrinkage and selection operator (LASSO), simultaneous orthogonal matching pursuit least squares (SOMP-LS) , simultaneous orthogonal matching pursuit total least squares (SOMP-TLS) and off-grid sparse Bayesian inference (OGSBI).
稀疏贝叶斯学习（SBL）被应用于互质阵列，用于欠定宽带波达方向（DOA）估计。通过使用增广协方差矩阵，互质阵列能够实现比物理传感器数量更多的自由度（DOF），以分辨更多的信号源。基于稀疏的DOA估计可能会降低检测和估计性能，因为无论搜索网格多么精细，信号源都可能不在搜索网格上。使用定点更新的SBL可以很好地解决这种字典失配问题。SBL可以自动选择稀疏性并近似解决非凸优化问题。进行了数值模拟，以验证基于互质阵列的SBL用于欠定宽带DOA估计的有效性。显然，与最小绝对收缩和选择算子（LASSO）、同时正交匹配追踪最小二乘法（SOMP-LS）、同时正交匹配追踪总体最小二乘法（SOMP-TLS）和离网格稀疏贝叶斯推理（OGSBI）相比，SBL在检测和估计方面能够获得良好的性能。
