非圆信号的贝叶斯稀疏重构阵列测向方法

对信号非圆特性的有效利用能显著改善子空间类阵列测向方法的性能，但难以弥补此类方法在低信噪比（SNR）、小样本等信号环境适应能力方面的局限。本文引入贝叶斯稀疏学习（SBL）技术以解决非圆信号的波达方向（DOA）估计问题，在结合信号非圆特性的同时对入射信号的空域稀疏性加以利用，通过将非圆信号阵列输出协方差矩阵和共轭协方差矩阵在预先定义的空域字典集上进行稀疏重构，得到入射信号的空间谱重构结果，并依据其谱峰位置估计各信号的方向。该方法对独立和相关信号都具有较好的适应能力，仿真结果验证了该方法在信号环境适应能力和相关信号测向精度等方面的优势。

Direction of Arrival Estimation Method of Non-circular Signals via Sparse Bayesian Reconstruction

The performance of the subspace-based direction of arrival (DOA) estimation methods can be improved significantly via effective exploitation of the non-circularity of the incident signals, but the shortcomings of these methods in adaptation to demanding scenarios, such as low signal-to-noise ratio (SNR) and limited snapshots, can hardly be made up. The sparse Bayesian learning (SBL) technique is introduced in this paper to deal with the DOA estimation problem of non-circular signals. The spatial sparsity of the incident signals is exploited together with their non-circularity property, and the covariance and conjugate covariance matrices of the array outputs of non-circular signals are decomposed jointly under a sparsity constraint to reconstruct the spatial spectrum of the incident signals, and the DOA estimates are finally obtained according to the spectrum peak locations. This method is robust against inter-signal correlation, and its superiorities in adaptation to demanding scenarios as well as in DOA estimation precision are demonstrated by the simulation results.