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We consider the problem of recovering block-sparse signals whose cluster patterns are unknown a priori. Block-sparse signals with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. However, the knowledge of the block partition is usually unavailable in practice. In this paper, we develop a new sparse Bayesian learning(More)
— Conventional compressed sensing theory assumes signals have sparse representations in a known, finite dictionary. Nevertheless, in many practical applications such as direction-of-arrival (DOA) estimation and line spectral estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a continuous domain. To apply the(More)
Conventional broadband beamforming structures make use of finite-impulse-response (FIR) filters in each channel. Large numbers of coefficients are required to retain the desired signal-to-interference-plus-noise-ratio (SINR) performance as the operating bandwidth increases. It has been proven that the optimal frequency-dependent array weighting of broadband(More)
This paper proposes a new second-order statistics-based method for blind multiple-input multiple-output (MIMO) finite-impulse-response (FIR) channel estimation driven by colored sources. It is assumed that the second-order statistics (SOS) of the input sources are known a priori. By exploiting the new derived properties of the companion matrices, an(More)
In this letter, a new broadband beamformer using infinite impulse response (IIR) filters is proposed. The unique feature of our letter is that by replacing all the delay elements with tap-to-tap IIR filters under some structural restrictions, the Frost processor is naturally extended from the finite impulse response (FIR) beamformer to the IIR beamformer.(More)
Approximate formulations for the 3-dB beamwidth are derived in this letter to measure the spatial resolution of the broadband nonredundant array (NRA) and minimum redundancy array (MRA), which assume the ideal continuous-time, infinite-length filters with the frequency responses obtained by the linearly constrained minimum variance (LCMV) optimization. By(More)
We propose a pattern-coupled sparse Bayesian learning method for inverse synthetic aperture radar (ISAR) imaging by exploiting a block-sparse structure inherent in ISAR target images. A two-dimensional pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring scatterers on the target scene. An(More)