Huiping Duan

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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)
In this paper, we consider the problem of blind multiple-input multiple-output (MIMO) finite impulse response (FIR) channel identification driven by spatially correlated signals. The second-order statistics (SOS) of the input sources are assumed known a priori. It is shown that under certain specified conditions, the MIMO FIR channel can be completely(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)
Conventional compressed sensing theory assumes signals have sparse representations in a known, finite dictionary. Nevertheless, in many practical applications such as directionof-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)
  • Huiping Duan
  • 2014 19th International Conference on Digital…
  • 2014
In order to recover an ensemble of signals which share a common sparsity pattern while being measured under different sensing matrices, we explore the MSM-FOCUSS algorithm by extending the well-known M-FOCUSS (FOCal Under-determined System Solver) approach from the MMV (Multiple-Measurement-Vectors) to the MSM (Multiple-Sensing-Matrices) scenario. The(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)
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)
Dictionary mismatch caused by finite spatial discretization and off-grid situation leads to performance degradation in sparse-representation-based direction-of-arrival (DOA) estimation. In this paper, a procedure implementing DOA estimation and rectification of dictionary in an alternating way is designed. A strategy using noise subspace fitting (NSF) is(More)