Interpretation of MUSIC for location detecting of small inhomogeneities surrounded by random scatterers
Won-Kwang Park
TL;DR
This paper analyzes the MUSIC algorithm's effectiveness in locating small electromagnetic inhomogeneities amidst random scatterers, revealing its structure and potential for enhancement through a mathematical relationship with Bessel functions.
Contribution
The paper establishes a rigorous relationship between the MUSIC imaging function and Bessel functions, providing insights into its properties and avenues for improvement.
Findings
MUSIC imaging function relates to zero-order Bessel functions.
The analysis explains previously unexplained phenomena.
The study suggests methods for enhancing MUSIC algorithm performance.
Abstract
In this paper, we consider the MUltiple SIgnal Classification (MUSIC) algorithm for identifying the locations of small electromagnetic inhomogeneities surrounded by random scatterers. For this purpose, we rigorously analyze the structure of MUSIC-type imaging function by establishing a relationship with zero-order Bessel function of the first kind. This relationship shows certain properties of the MUSIC algorithm, explains some unexplained phenomena, and provides a method for improvements.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.