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--- a/paper/paper.tex
+++ b/paper/paper.tex
@@ -1,4 +1,4 @@
-% Created 2020-10-05 lun. 15:33
+% Created 2020-10-25 dim. 10:05
 % Intended LaTeX compiler: pdflatex
 \documentclass[conference]{IEEEtran}
 \usepackage[utf8]{inputenc}
@@ -35,7 +35,7 @@
 \def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
 \usepackage{showframe}
 \author{\IEEEauthorblockN{Dehaeze Thomas} \IEEEauthorblockA{\textit{European Synchrotron Radiation Facility} \\ Grenoble, France\\ \textit{Precision Mechatronics Laboratory} \\ \textit{University of Liege}, Belgium \\ thomas.dehaeze@esrf.fr }\and \IEEEauthorblockN{Collette Christophe} \IEEEauthorblockA{\textit{BEAMS Department}\\ \textit{Free University of Brussels}, Belgium\\ \textit{Precision Mechatronics Laboratory} \\ \textit{University of Liege}, Belgium \\ ccollett@ulb.ac.be }}
-\date{2020-10-05}
+\date{2020-10-25}
 \title{Optimal and Robust Sensor Fusion}
 \begin{document}
 
@@ -50,7 +50,7 @@ Complementary Filters, Sensor Fusion, H-Infinity Synthesis
 \end{IEEEkeywords}
 
 \section{Introduction}
-\label{sec:org26a7400}
+\label{sec:org2a4e2c2}
 \label{sec:introduction}
 
 \begin{itemize}
@@ -61,11 +61,11 @@ Complementary Filters, Sensor Fusion, H-Infinity Synthesis
 \end{itemize}
 
 \section{Optimal Super Sensor Noise: \(\mathcal{H}_2\) Synthesis}
-\label{sec:org49e80fd}
+\label{sec:orgb0fb3f0}
 \label{sec:optimal_fusion}
 
 \subsection{Sensor Model}
-\label{sec:org9555932}
+\label{sec:org9e4a17b}
 Let's consider a sensor measuring a physical quantity \(x\) (Figure \ref{fig:sensor_model_noise}).
 The sensor has an internal dynamics which is here modelled with a Linear Time Invariant (LTI) system transfer function \(G_i(s)\).
 
@@ -101,7 +101,7 @@ In order to obtain an estimate \(\hat{x}_i\) of \(x\), a model \(\hat{G}_i\) of
 \end{figure}
 
 \subsection{Sensor Fusion Architecture}
-\label{sec:orga12ae12}
+\label{sec:orge7841b3}
 Let's now consider two sensors measuring the same physical quantity \(x\) but with different dynamics \((G_1, G_2)\) and noise characteristics \((N_1, N_2)\) (Figure \ref{fig:sensor_fusion_noise_arch}).
 
 The noise sources \(\tilde{n}_1\) and \(\tilde{n}_2\) are considered to be uncorrelated.
@@ -138,7 +138,7 @@ In such case, the super sensor estimate \(\hat{x}\) is equal to \(x\) plus the n
 \end{equation}
 
 \subsection{Super Sensor Noise}
-\label{sec:org924b750}
+\label{sec:orge42a7c0}
 Let's note \(n\) the super sensor noise.
 \begin{equation}
   n = \left( H_1 N_1 \right) \tilde{n}_1 + \left( H_2 N_2 \right) \tilde{n}_2
@@ -152,7 +152,7 @@ As the noise of both sensors are considered to be uncorrelated, the PSD of the s
 It is clear that the PSD of the super sensor depends on the norm of the complementary filters.
 
 \subsection{\(\mathcal{H}_2\) Synthesis of Complementary Filters}
-\label{sec:org042a601}
+\label{sec:org150fd28}
 The goal is to design \(H_1(s)\) and \(H_2(s)\) such that the effect of the noise sources \(\tilde{n}_1\) and \(\tilde{n}_2\) has the smallest possible effect on the noise \(n\) of the estimation \(\hat{x}\).
 
 And the goal is the minimize the Root Mean Square (RMS) value of \(n\):
@@ -196,7 +196,7 @@ We then have that the \(\mathcal{H}_2\) synthesis applied on \(P_{\mathcal{H}_2}
 \end{figure}
 
 \subsection{Example}
-\label{sec:org98c54c2}
+\label{sec:org4abe5c3}
 
 \begin{figure}[htbp]
 \centering
@@ -232,7 +232,7 @@ We then have that the \(\mathcal{H}_2\) synthesis applied on \(P_{\mathcal{H}_2}
 \end{figure}
 
 \subsection{Robustness Problem}
-\label{sec:org81a0772}
+\label{sec:org1116fe0}
 
 \begin{figure}[htbp]
 \centering
@@ -247,11 +247,11 @@ We then have that the \(\mathcal{H}_2\) synthesis applied on \(P_{\mathcal{H}_2}
 \end{figure}
 
 \section{Robust Sensor Fusion: \(\mathcal{H}_\infty\) Synthesis}
-\label{sec:org78ced60}
+\label{sec:orgcf4e02a}
 \label{sec:robust_fusion}
 
 \subsection{Representation of Sensor Dynamical Uncertainty}
-\label{sec:org9df3b01}
+\label{sec:org45ee620}
 
 In Section \ref{sec:optimal_fusion}, the model \(\hat{G}_i(s)\) of the sensor was considered to be perfect.
 In reality, there are always uncertainty (neglected dynamics) associated with the estimation of the sensor dynamics.
@@ -271,7 +271,7 @@ The sensor can then be represented as shown in Figure \ref{fig:sensor_model_unce
 \end{figure}
 
 \subsection{Sensor Fusion Architecture}
-\label{sec:orgf4531ff}
+\label{sec:orgec549bc}
 Let's consider the sensor fusion architecture shown in Figure \ref{fig:sensor_fusion_arch_uncertainty} where the dynamical uncertainties of both sensors are included.
 
 The super sensor estimate is then:
@@ -296,7 +296,7 @@ As \(H_1\) and \(H_2\) are complementary filters, we finally have:
 \end{figure}
 
 \subsection{Super Sensor Dynamical Uncertainty}
-\label{sec:orgf5bb33e}
+\label{sec:org6867184}
 The uncertainty set of the transfer function from \(\hat{x}\) to \(x\) at frequency \(\omega\) is bounded in the complex plane by a circle centered on 1 and with a radius equal to \(|W_1(j\omega) H_1(j\omega)| + |W_2(j\omega) H_2(j\omega)|\) as shown in Figure \ref{fig:uncertainty_set_super_sensor}.
 
 
@@ -311,7 +311,7 @@ And we can see that the dynamical uncertainty of the super sensor is equal to th
 At frequencies where \(\left|W_i(j\omega)\right| > 1\) the uncertainty exceeds \(100\%\) and sensor fusion is impossible.
 
 \subsection{\(\mathcal{H_\infty}\) Synthesis of Complementary Filters}
-\label{sec:orgf07efa7}
+\label{sec:org9cbbe5b}
 In order for the fusion to be ``robust'', meaning no phase drop will be induced in the super sensor dynamics,
 
 The goal is to design two complementary filters \(H_1(s)\) and \(H_2(s)\) such that the super sensor noise uncertainty is kept reasonably small.
@@ -357,7 +357,7 @@ The \(\mathcal{H}_\infty\) norm of Eq. \eqref{eq:Hinf_norm} is equals to \(\sigm
 \end{figure}
 
 \subsection{Example}
-\label{sec:org0ca6ef9}
+\label{sec:orgfc0d330}
 
 \begin{figure}[htbp]
 \centering
@@ -392,11 +392,11 @@ The \(\mathcal{H}_\infty\) norm of Eq. \eqref{eq:Hinf_norm} is equals to \(\sigm
 
 
 \section{Optimal and Robust Sensor Fusion: Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
-\label{sec:orgf642e73}
+\label{sec:org81d3977}
 \label{sec:optimal_robust_fusion}
 
 \subsection{Sensor with noise and model uncertainty}
-\label{sec:org8949812}
+\label{sec:orgcd51fc4}
 We wish now to combine the two previous synthesis, that is to say
 
 The sensors are now modelled by a white noise with unitary PSD \(\tilde{n}_i\) shaped by a LTI transfer function \(N_i(s)\).
@@ -417,7 +417,7 @@ Multiplying by the inverse of the nominal model of the sensor dynamics gives an
 \end{figure}
 
 \subsection{Sensor Fusion Architecture}
-\label{sec:orgcbc3d54}
+\label{sec:org32c4c98}
 
 For reason of space, the blocks \(\hat{G}_i\) and \(\hat{G}_i^{-1}\) are omitted.
 
@@ -444,7 +444,7 @@ The estimate \(\hat{x}\) of \(x\)
 \end{figure}
 
 \subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
-\label{sec:org9d3f160}
+\label{sec:org73e0335}
 
 The synthesis objective is to generate two complementary filters \(H_1(s)\) and \(H_2(s)\) such that the uncertainty associated with the super sensor is kept reasonably small and such that the RMS value of super sensors noise is minimized.
 
@@ -479,7 +479,7 @@ The synthesis objective is to:
 \end{figure}
 
 \subsection{Example}
-\label{sec:org85f304b}
+\label{sec:orga68c808}
 
 \begin{figure}[htbp]
 \centering
@@ -506,27 +506,27 @@ The synthesis objective is to:
 \end{figure}
 
 \section{Experimental Validation}
-\label{sec:org49bf34a}
+\label{sec:orga4af6ce}
 \label{sec:experimental_validation}
 
 \subsection{Experimental Setup}
-\label{sec:orgdd8fce6}
+\label{sec:orgab10fd3}
 
 \subsection{Sensor Noise and Dynamical Uncertainty}
-\label{sec:org21add72}
+\label{sec:orgc6d5bae}
 
 \subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
-\label{sec:org30521a3}
+\label{sec:orga5c7815}
 
 \subsection{Super Sensor Noise and Dynamical Uncertainty}
-\label{sec:org86cde79}
+\label{sec:orgd7da409}
 
 \section{Conclusion}
-\label{sec:org16245b7}
+\label{sec:org6eddbc8}
 \label{sec:conclusion}
 
 \section{Acknowledgment}
-\label{sec:orgd992049}
+\label{sec:org44ed488}
 
 \bibliography{ref}
 \end{document}