977 citations · 5.1k across the 105 of their papers we have counts for
7 papers · 1 filter
Distributed Parameter Estimation with Quantized Communication via Running Average
Shanying Zhu, Yeng Chai Soh, Lihua Xie
In this paper, we consider the parameter estimation problem over sensor networks in the presence of quantized data and directed communication links. We propose a two-stage algorith…
Achieving High Resolution for Super-resolution via Reweighted Atomic Norm Minimization
Zai Yang, Lihua Xie
The super-resolution theory developed recently by Candès and Fernandes-Granda aims to recover fine details of a sparse frequency spectrum from coarse scale information only. The th…
Optimization-based State Estimation under Bounded Disturbances
Wuhua Hu, Lihua Xie, Keyou You
This paper studies an optimization-based state estimation approach for discrete-time nonlinear systems under bounded process and measurement disturbances. We first introduce a full…
Enhancing Sparsity and Resolution via Reweighted Atomic Norm Minimization
Zai Yang, Lihua Xie
The mathematical theory of super-resolution developed recently by Candès and Fernandes-Granda states that a continuous, sparse frequency spectrum can be recovered with infinite pre…
On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data
Zai Yang, Lihua Xie
This paper is concerned about sparse, continuous frequency estimation in line spectral estimation, and focused on developing gridless sparse methods which overcome grid mismatches…
Continuous Compressed Sensing With a Single or Multiple Measurement Vectors
Zai Yang, Lihua Xie
We consider the problem of recovering a single or multiple frequency-sparse signals, which share the same frequency components, from a subset of regularly spaced samples. The probl…
Exact Joint Sparse Frequency Recovery via Optimization Methods
Zai Yang, Lihua Xie
Frequency recovery/estimation from discrete samples of superimposed sinusoidal signals is a classic yet important problem in statistical signal processing. Its research has recentl…