Application of MUSIC algorithm in real-world microwave imaging of unknown anomalies from scattering matrix

TL;DR

This paper explores the use of the MUSIC algorithm for non-iterative microwave imaging of unknown anomalies using scattering matrix data, supported by mathematical analysis and real-data simulations.

Contribution

It establishes a mathematical relationship between MUSIC and Bessel functions, demonstrating its feasibility for microwave imaging of small anomalies.

Findings

MUSIC algorithm effectively images small anomalies from scattering data.

Mathematical analysis links MUSIC to Bessel functions, explaining its imaging capabilities.

Simulation results confirm the algorithm's practical feasibility at 925 MHz.

Abstract

In this contribution, we consider MUltiple SIgnal Classification (MUSIC)-type algorithm for a non-iterative microwave imaging of small and arbitrary shaped extended anomalies located in a homogeneous media from scattering matrix whose elements are scattering parameters measured at dipole antennas. In order to explain the feasibility of MUSIC in microwave imaging, we investigate mathematical structure of MUSIC by establishing a relationship with an infinite series of Bessel function of integer order and antennas setting. This is based on the representation formula of scattering parameters in the presence of small anomalies and the application of Born approximation. Simulation results using real-data at $f=925$MHz of angular frequency are exhibited to show the feasibility of designed algorithm and to support investigated structure of imaging function.