Imperial College London
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ProfessorPier LuigiDragotti

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6192p.dragotti

 
 
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Location

 

814Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Dragotti:2017:10.1109/TSP.2017.2742983,
author = {Dragotti, P and Murray-Bruce, J},
doi = {10.1109/TSP.2017.2742983},
journal = {IEEE Transactions on Signal Processing},
pages = {6365--6380},
title = {A Sampling Framework for Solving Physics-driven Inverse Source Problems},
url = {http://dx.doi.org/10.1109/TSP.2017.2742983},
volume = {65},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Partial differential equations are central to describing many physical phenomena. In many applications these phenomena are observed through a sensor network, with the aim of inferring its underlying properties. Leveraging from certain results in sampling and approximation theory, we present a new framework for solving a class of inverse source problems for physical fields governed by linear partial differential equations. Specifically, we demonstrate that the unknown field sources can be recovered from a sequence of, so called, generalised measurements by using multidimensional frequency estimation techniques. Next we show that---for physics-driven fields---this sequence of generalised measurements can be estimated by computing a linear weighted-sum of the sensor measurements; whereby the exact weights (of the sums) correspond to those that reproduce multidimensional exponentials, when used to linearly combine translates of a particular prototype function related to the Green's function of our underlying field. Explicit formulae are then derived for the sequence of weights, that map sensor samples to the exact sequence of generalised measurements when the Green's function satisfies the generalised Strang-Fix condition. Otherwise, the same mapping yields a close approximation of the generalised measurements. Based on this new framework we develop practical, noise robust, sensor network strategies for solving the inverse source problem, and then present numerical simulation results to verify their performance.
AU  - Dragotti,P
AU  - Murray-Bruce,J
DO  - 10.1109/TSP.2017.2742983
EP  - 6380
PY  - 2017///
SN  - 1053-587X
SP  - 6365
TI  - A Sampling Framework for Solving Physics-driven Inverse Source Problems
T2  - IEEE Transactions on Signal Processing
UR  - http://dx.doi.org/10.1109/TSP.2017.2742983
UR  - http://hdl.handle.net/10044/1/50271
VL  - 65
ER  -
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