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Model-Based Signal Processing for GPR Data Inversion: MUSIC Algorithm Overview

This article discusses the use of the MUSIC algorithm for Ground Penetrating Radar (GPR) data inversion, aiming to estimate geo-physical parameters by exploiting orthogonality of signal and noise. The article covers the principle of permittivity contrast in layered media, simulations of permittivity profiles, and various inversion techniques, including the Layer Stripping Technique. It also explores the challenges of weaker returns, false alarms, and convergence issues in model-based inversion. The MUSIC algorithm enhances valid returns and suppresses noise peaks but requires a reliable propagation model.

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Model-Based Signal Processing for GPR Data Inversion: MUSIC Algorithm Overview

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  1. Courtesy: JPL, NASA Weaker returns cannot be resolved Weaker returns are resolved Model Based Signal Processing for GPR Data Inversion MUSIC Algorithm Overview Objective: Estimation of geo-physical parameters from Ground Penetrating Radar (GPR) data – also called Electro- Magnetic (EM) Inversion Principle: Permittivity contrast in layered media causes reflection of incident Electromagnetic Wave • MUSIC : MUltipleSIgnalClassification • Super resolution frequency estimation technique • Exploits Orthogonality of signal and noise • Enhances valid returns and suppresses noise peaks • Requires reliable propagation model Simulations Geo-Physical Profile PermittivityProfile Radar System Reconstructed Vs actual Permittivity profile Range profiles obtained using FFT & MUSIC A typical dielectric profile Inversion Techniques Inversion on Actual Radar Data • Layer Stripping Technique • Apply Fourier/Inverse Fourier Transform on received signal • Set threshold and locate position and amplitudes of interfaces • Reflection amplitudes and time delays are related to permittivity • Recursively estimate permittivity profile • Wide Band FMCW Radar (2 – 8 GHz) used in Antarctica to estimate snow thickness • MUSIC algorithm was used for enhancement and permittivity estimation • Core data was modeled into permittivity profile using theoretical models • Problem of False alarms and missed peaks • Weaker returns are buried under side-lobes of stronger returns Model Based Inversion • Based on MMSE Minimization: Gauss Newton Method Based on Spectral Estimation: MUSIC Algorithm . Convergence issues – produces unstable/un-useful solutions . Problem with depth discretization . Requires good SNR Works best ! Reconstructed Vs actual permittivity profile Range profiles obtained using FFT & MUSIC GUI for the inversion algorithm 040105

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