Direction-of-Arrival Estimation for Constant Modulus Signals via Convex Optimization

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Several man-made signals in communications and array processing, e.g., phase-modulated and frequency-modulated signals, exhibit the constant modulus (CM) property. This paper is concerned about the problem of direction-of-arrival (DOA) estimation for CM source signals using linear arrays. Existing methods either rely on nonconvex optimization suffering from convergence or optimality issues or cannot fully use the CM property of signals or the array manifold due to great challenges brought by the highly nonconvex CM constraints. In this paper, we propose a convex optimization approach for CM DOA estimation based on atomic norm minimization. Our main contributions are summarized below. 1) To use the CM property, we define a CM atomic norm and formulate the CM DOA estimation problem as an ANM problem. 2) We propose a semidefinite programming, by introducing a series of positive-semidefinite structured matrices, to characterize the CM atomic norm and enable computations of the CM ANM problem. 3) Simulations are carried out that validate the advantageous performance of the proposed approach.

Original languageEnglish
Title of host publication2024 Photonics and Electromagnetics Research Symposium, PIERS 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350375909
DOIs
StatePublished - 2024
Event2024 Photonics and Electromagnetics Research Symposium, PIERS 2024 - Chengdu, China
Duration: 21 Apr 202425 Apr 2024

Publication series

Name2024 Photonics and Electromagnetics Research Symposium, PIERS 2024 - Proceedings

Conference

Conference2024 Photonics and Electromagnetics Research Symposium, PIERS 2024
Country/TerritoryChina
CityChengdu
Period21/04/2425/04/24

Fingerprint

Dive into the research topics of 'Direction-of-Arrival Estimation for Constant Modulus Signals via Convex Optimization'. Together they form a unique fingerprint.

Cite this