Bayesian filtering framework for noise characterization of frequency combs
Journal article, 2020

Amplitude and phase noise correlation matrices are of fundamental importance for studying noise properties of frequency combs. They include information about the origin of noise sources as well as the scaling and correlation of the noise across the comb lines. These matrices provide an insight that is essential for obtaining low-noise performance which is important for, e.g., applications in optical communication, low–noise microwave signal generation, and distance measurements. Estimation of amplitude and phase noise correlation matrices requires highly–accurate measurement technique which can distinguishes between noise sources coming from the frequency comb and the measurement system itself. Bayesian filtering provides a theoretically optimum approach for filtering of measurement noise and thereby, the most accurate measurement of phase and amplitude noise. In this paper, a novel Bayesian filtering based framework for joint estimation of amplitude and phase noise of multiple frequency comb lines is proposed, and demonstrated for phase noise characterization. Compared to the conventional approaches, that do not employ any measurement noise filtering, the proposed approach provides significantly more accurate measurements of correlation matrices, operates over a wide range of signal–to–noise–ratios and gives an insight into comb’s dynamics at short scales (<10−8 s). © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Phase noise

Bayesian filtering frameworks

Optical communication

Frequency estimation

Author

Giovanni Brajato

Technical University of Denmark (DTU)

Lars Lundberg

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Victor Torres Company

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Magnus Karlsson

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

D. Zibar

Technical University of Denmark (DTU)

Optics Express

1094-4087 (ISSN)

Vol. 28 9 13949-13964

Subject Categories

Medical Laboratory and Measurements Technologies

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1364/OE.391165

More information

Latest update

5/29/2020