Estimation of the quantities of harmful substances emitted into the atmosphere is one of the main challenges in modern environmental sciences. In most of the cases, this estimation requires solving a linear inverse problem. A key difficulty in evaluating the performance of any algorithm to solve this linear inverse problem is that the ground truth is typically unknown. In this paper we show that the noise encountered in this linear inverse problem is non-Gaussian. Next, we develop an algorithm to deal with the strong outliers present in the measurements. Finally, we test our approach on three different experiments: a simple synthetic experiment, a controlled real-world experiment, and real data from the Fukushima nuclear accident.
This repository contains the code to reproduce the results of the paper: "Outlier removal for improved source estimation in atmospheric inverse problems", By M. Martinez-Camara, A. Stohl and M. Vetterli, ICASSP 2014, Florence (Italy)
Andreas Stohl is with the Norwegian Institute for Air Research (NILU).
To generate the results showed in the figures 2, 3 and 5, run the following Matlab scripts:
RR_icassp_2014_fig2.m RR_icassp_2014_fig3.m RR_icassp_2014_fig5.m
To run them, you need the CVX package.
Copyright (c) 2014, Marta Martinez-Camara, Andreas Stohl, Martin Vetterli
This code is free to reuse for non-commercial purpose such as academic or educational. For any other use, please contact the authors.
Outlier removal for improved source estimation in atmospheric inverse problems by Marta Martinez-Camara, Andreas Stohl, Martin Vetterli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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