10 accepted papers
The unlimited sampling framework, utilizing modulo analog-to-digital converters (ADCs), has recently been introduced to mitigate the information loss caused by the dynamic range limitations of traditional ADCs. In this paper, we evaluate the noise sensitivity of unlimited sampling in line spectral e…
In direction-of-arrival (DOA) estimation, both stochastic and deterministic priors of source signals have been used to localize more sources than sensors. The stochastic prior refers to uncorrelateness of sources, while the deterministic one includes the constant modulus (CM) property of sources. Ex…
Multichannel spectral compressed sensing is a fundamental problem in statistical signal processing. In order to reduce the hardware cost and energy consumption, one-bit multichannel spectral compressed sensing is considered. Inspired by rewighted atomic norm minimization, we propose a new method to…
This paper focuses on the transmit and receive beamforming design of an integrated sensing and communication system. In particular, a base station transmits waveform for simultaneous downlink multiuser communication as well as radar sensing, and spatial filter is performed for receive echos to reduc…
We study the spectral super-resolution problem, which concerns the construction of an undamped spectrally sparse signal and its frequencies from its partially revealed entries. We propose a nonconvex method composed of a Hankel-Toeplitz matrix factorization model and a gradient descent algorithm ter…
DOA estimation with sparse linear arrays has been extensively studied, with an emphasis on localizing more sources than sensors. A critical assumption in previous studies however is that the sources are all uncorrelated. In this paper, we present an algorithm that is shown to be able to localize mor…
Spatial smoothing is a common preprocessing scheme for subspace methods that resolves their sensitivity to coherent sources. The source resolvability problem of spatial-smoothing-based subspace methods has been extensively investigated using different analysis techniques. In this paper, a unified Ha…
This paper concerns the line spectral estimation problem within the recent super-resolution framework. The frequencies of interest are assumed to follow a prior probability distribution. To effectively and efficiently exploit the prior information, we devise a weighted atomic norm approach that is p…
The authors have recently proposed two kinds of gridless sparse methods for direction of arrival (DOA) estimation that exploit joint sparsity among snapshots and completely resolve the grid mismatch issue of previous grid-based sparse methods. One is based on covariance fitting from a statistical pe…
The super-resolution theory developed recently by Candès and Fernandes-Granda aims to recover fine details in a sparse frequency spectrum from coarse scale information. The theory was then extended to the cases of compressive samples and/or multiple measurement vectors. However, the existing atomic…