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

Abstract

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≤false(M4−2M3+7M2−6Mfalse)/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 Ofalse(M4false), where M is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method.

On the Complementarity of Sparse L0 and CEL0 Regularized Loss Landscapes for DOA Estimation

“…They also often require a priori knowledge of the number of sources and need a sufficient number of snapshots. Sparse DOA estimation has received much attention in the last decade due to its potential performance in such scenarios [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ].…”

Parameter estimation of underwater impulsive noise with the Class B model

“…Generally, the noise distribution should be modelled prior to designing optimal signal processors. In [12], the noise is analysed based on the higher‐order statistical characteristics such as the higher‐order cumulants. On the one hand, it brings a large computation load.…”

Vector Hydrophone Array Design Based on Off-Grid Compressed Sensing

“…If the optimal array position does not fall on the predetermined grid, it is called grid mismatch. The phenomenon has been studied and a lot of direction of arrival (DOA) estimation algorithms based on off-grid have been proposed [26][27][28][29][30][31][32][33][34][35]. Bayesian compressed sensing has also been widely used in this field [30][31][32].…”