基于Bessel先验快速稀疏贝叶斯学习的互质阵列DOA估计

为提高低采样点条件下互质阵列DOA估计精度，该文提出基于Bessel先验快速稀疏贝叶斯学习算法。该方法针对互质阵列输出的多采样点复数数据，首先构建了基于Bessel先验的多量测分层模型；其次推导了模型所涉超参数的对数似然函数，根据最大似然估计准则得到了超参数的迭代公式；最后提出了快速实现方案，提高了运算效率。仿真结果表明，该方法不依赖先验信息，在低采样点条件下具有更高的DOA估计精度和分辨率，能够对相干信号进行高精度DOA估计，并具有较高的运算效率。此外，该文探究了虚拟阵列扩展与互质阵列测向自由度扩展间的关联，为后续阵列误差条件下互质阵列DOA研究估计提供参考。

DOA Estimation for Co-prime Array Based on Fast Sparse Bayesian Learning Using Bessel Priors

In order to improve DOA estimation accuracy of co-prime array while the number of snapshots is small, a novel fast Sparse Bayesian Learning (SBL) algorithm using Bessel priors is proposed. Focusing on the multi-snapshots complex output data of coprime array, a multiple measurement vectors hierarchical model based on Bessel priors is firstly built. Then the log-likelihood function of model hyperparameters is derived, and the iterative formulas of hyperparameters are derived based on the criterion of maximum likelihood estimation. Finally, a fast implementation scheme is developed in order to improve the computation efficiency. Simulation experiments show that the proposed algorithm is independent on prior information. Under the condition of small number of snapshots, higher DOA estimation accuracy and resolution of uncorrelated and correlated signals can be achieved with proper computational efficiency. Further more, the necessity between virtual array extension and DOA estimation freedom of co-prime array is explored, which provides reference to DOA estimation for co-prime array under array perturbation conditions.