Underdetermined wideband direction of arrival (DOA) estimation based on the sparse array is studied here and a novel algorithm is developed to improve the estimation performance of off-grid targets in the framework of sparse Bayesian learning. First, the narrowband off-grid model is extended to a wideband case and the sparse Bayesian model containing off-grid biases is deduced. Then, a sequential solution is proposed to obtain the estimation, where the fast sparse Bayesian learning strategy is employed to improve the computational efficiency. The estimation accuracy is improved significantly through off-grid compensation and the computational complexity is reduced remarkably. Simulation results verify the effectiveness of the proposed method.

中文翻译：

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离网目标的欠定宽带DOA估计：一种计算有效的稀疏贝叶斯学习方法

本文研究了基于稀疏阵列的欠定宽带到达方向估计，并在稀疏贝叶斯学习框架内开发了一种新的算法来提高离网目标的估计性能。首先，将窄带离网模型扩展到宽带情况，并推导包含离网偏差的稀疏贝叶斯模型。然后，提出了一种序列求解方法以获得估计值，其中采用了快速稀疏贝叶斯学习策略来提高计算效率。通过离网补偿显着提高了估计精度，并显着降低了计算复杂度。仿真结果验证了该方法的有效性。