DeepAI AI Chat
Log In Sign Up

Two-snapshot DOA Estimation via Hankel-structured Matrix Completion

by   Mohammad Bokaei, et al.

In this paper, we study the problem of estimating the direction of arrival (DOA) using a sparsely sampled uniform linear array (ULA). Based on an initial incomplete ULA measurement, our strategy is to choose a sparse subset of array elements for measuring the next snapshot. Then, we use a Hankel-structured matrix completion to interpolate for the missing ULA measurements. Finally, the source DOAs are estimated using a subspace method such as Prony on the fully recovered ULA. We theoretically provide a sufficient bound for the number of required samples (array elements) for perfect recovery. The numerical comparisons of the proposed method with existing techniques such as atomic-norm minimization and off-the-grid approaches confirm the superiority of the proposed method.


page 1

page 2

page 3

page 4


Fast Two-Dimensional Atomic Norm Minimization in Spectrum Estimation and Denoising

Motivated by recent work on two dimensional (2D) harmonic component reco...

Structured Matrix Completion with Applications to Genomic Data Integration

Matrix completion has attracted significant recent attention in many fie...

Recover the lost Phasor Measurement Unit Data Using Alternating Direction Multipliers Method

This paper presents a novel algorithm for recovering missing data of pha...

Joint Block Low Rank and Sparse Matrix Recovery in Array Self-Calibration Off-Grid DoA Estimation

This letter addresses the estimation of directions-of-arrival (DoA) by a...

On Inferences from Completed Data

Matrix completion has become an extremely important technique as data sc...

Matrix Completion with Model-free Weighting

In this paper, we propose a novel method for matrix completion under gen...