Sparse Method for Direction of Arrival Estimation Using Denoised Fourth-Order Cumulants Vector

作者：Fan, Yangyu; Wang, Jianshu^*; Du, Rui; Lv, Guoyun

来源：Sensors (Switzerland), 2018, 18(6): 1815.

DOI：10.3390/s18061815

• direction of arrival estimation
• fourth-order cumulants
• non-Gaussian sources
• fourth-order difference co-array
• sparse Bayesian learning

Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting parameters. In this paper, a novel FOCs vector-based sparse DOA estimation method is proposed. Firstly, by utilizing the concept of a fourth-order difference co-array (FODCA), an advanced FOCs vector denoising or dimension reduction procedure is presented for arbitrary array geometries. Then, a novel single measurement vector (SMV) model is established by the denoised FOCs vector, and efficiently solved by an off-grid sparse Bayesian inference (OGSBI) method. The estimation errors of FOCs are integrated in the SMV model, and are approximately estimated in a simple way. A necessary condition regarding the number of identifiable sources of our method is presented that, in order to uniquely identify all sources, the number of sources K must fulfill K(M4-2M3+7M2-6M)/8. The proposed method suits any geometry, does not need prior knowledge of the number of sources, is insensitive to associated parameters, and has maximum identifiability O(M4), where M is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method.

• 出版日期2018-6
• 单位西北工业大学