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On the Sample Complexity of Multichannel Frequency Estimation via Convex Optimization
Zai Yang, Jinhui Tang, Yonina C. Eldar +1
The use of multichannel data in line spectral estimation (or frequency estimation) is common for improving the estimation accuracy in array processing, structural health monitoring…
Sparse Methods for Direction-of-Arrival Estimation
Zai Yang, Jian Li, Petre Stoica +1
Direction-of-arrival (DOA) estimation refers to the process of retrieving the direction information of several electromagnetic waves/sources from the outputs of a number of receivi…
Towards a Unified Resilience Analysis: State Estimation against Integrity Attacks
Duo Han, Yilin Mo, Lihua Xie
We consider the problem of resilient state estimation in the presence of integrity attacks. There are m sensors monitoring the state and p of them are under attack. The sensory dat…
Frequency-Selective Vandermonde Decomposition of Toeplitz Matrices with Applications
Zai Yang, Lihua Xie
The classical result of Vandermonde decomposition of positive semidefinite Toeplitz matrices, which dates back to the early twentieth century, forms the basis of modern subspace an…
An Improved DOA Estimation Algorithm for Circular and Non-Circular Signals with High Resolution
Liangtian Wan, Lihua Xie
In this paper, an improved direction-of-arrival (DOA) estimation algorithm for circular and non-circular signals is proposed. Most state-of-the-art algorithms only deal with the DO…
Robust State Estimation against Sparse Integrity Attacks
Duo Han, Yilin Mo, Lihua Xie
We consider the problem of robust state estimation in the presence of integrity attacks. There are sensors monitoring a dynamical process. Subject to the integrity attacks, …
Convex Optimization Based State Estimation against Sparse Integrity Attacks
Duo Han, Yilin Mo, Lihua Xie
We consider the problem of robust estimation in the presence of integrity attacks. There are m sensors monitoring the state and p of them are under attack. The malicious measuremen…
Distributed Inference for Relay-Assisted Sensor Networks With Intermittent Measurements Over Fading Channels
Shanying Zhu, Yeng Chai Soh, Lihua Xie
In this paper, we consider a general distributed estimation problem in relay-assisted sensor networks by taking into account time-varying asymmetric communications, fading channels…
Vandermonde Decomposition of Multilevel Toeplitz Matrices with Application to Multidimensional Super-Resolution
Zai Yang, Lihua Xie, Petre Stoica
The Vandermonde decomposition of Toeplitz matrices, discovered by Carathéodory and Fejér in the 1910s and rediscovered by Pisarenko in the 1970s, forms the basis of modern subspace…
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…
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…
A discretization-free sparse and parametric approach for linear array signal processing
Zai Yang, Lihua Xie, Cishen Zhang
Direction of arrival (DOA) estimation in array processing using uniform/sparse linear arrays is concerned in this paper. While sparse methods via approximate parameter discretizati…
Bayesian compressed sensing with new sparsity-inducing prior
Zai Yang, Lihua Xie, Cishen Zhang
Sparse Bayesian learning (SBL) is a popular approach to sparse signal recovery in compressed sensing (CS). In SBL, the signal sparsity information is exploited by assuming a sparsi…
Variational Bayesian algorithm for quantized compressed sensing
Zai Yang, Lihua Xie, Cishen Zhang
Compressed sensing (CS) is on recovery of high dimensional signals from their low dimensional linear measurements under a sparsity prior and digital quantization of the measurement…
Robustly Stable Signal Recovery in Compressed Sensing with Structured Matrix Perturbation
Zai Yang, Cishen Zhang, Lihua Xie
The sparse signal recovery in the standard compressed sensing (CS) problem requires that the sensing matrix be known a priori. Such an ideal assumption may not be met in practical…
On Phase Transition of Compressed Sensing in the Complex Domain
Zai Yang, Cishen Zhang, Lihua Xie
The phase transition is a performance measure of the sparsity-undersampling tradeoff in compressed sensing (CS). This letter reports our first observation and evaluation of an empi…