Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
IEEE Transactions on Signal Processing - Part II
Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling
IEEE Transactions on Signal Processing
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A new method based on a novel model for off-grid direction-of-arrival (DOA) estimation is presented. The novel model is based on the sample covariance matrix and the off-grid representation of the steering vector. Based on this model, its equivalent signals are assumed to satisfy independent Gaussian distribution and its noise variance can be normalized to 1. The off-grid DOAs are estimated by the block sparse Bayesian algorithm. The advantages of the proposed method are that it considers the temporal correlation existed in each row of the equivalent signal sample matrix and the normalized noise variance does not need to be estimated. Moreover, this algorithm can work without the knowledge of the number of signals. Numerical simulations demonstrate the superior performance of the proposed method.