Rename paper files

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Thomas Dehaeze 2021-09-10 11:05:29 +02:00
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#!/bin/env perl
# Shebang is only to get syntax highlighting right across GitLab, GitHub and IDEs.
# This file is not meant to be run, but read by `latexmk`.
# ======================================================================================
# Perl `latexmk` configuration file
# ======================================================================================
# ======================================================================================
# PDF Generation/Building/Compilation
# ======================================================================================
@default_files=('dehaeze22_optim_robus_compl_filte.tex');
# PDF-generating modes are:
# 1: pdflatex, as specified by $pdflatex variable (still largely in use)
# 2: postscript conversion, as specified by the $ps2pdf variable (useless)
# 3: dvi conversion, as specified by the $dvipdf variable (useless)
# 4: lualatex, as specified by the $lualatex variable (best)
# 5: xelatex, as specified by the $xelatex variable (second best)
$pdf_mode = 1;
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# This is only checked in the *last* run, since naturally, there are undefined references
# in initial runs.
# This setting is potentially annoying when debugging/editing, but highly desirable
# in the CI pipeline, where such a warning should result in a failed pipeline, since the
# final document is incomplete/corrupted.
#
# However, I could not eradicate all warnings, so that `latexmk` currently fails with
# this option enabled.
# Specifically, `microtype` fails together with `fontawesome`/`fontawesome5`, see:
# https://tex.stackexchange.com/a/547514/120853
# The fix in that answer did not help.
# Setting `verbose=silent` to mute `microtype` warnings did not work.
# Switching between `fontawesome` and `fontawesome5` did not help.
$warnings_as_errors = 0;
# Show used CPU time. Looks like: https://tex.stackexchange.com/a/312224/120853
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# Default is 5; we seem to need more owed to the complexity of the document.
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# `0` is off.
# Testing in VSCode w/ LaTeX Workshop only worked for the compressed version.
# Adjust this as needed. Of course, only relevant for local use, no effect on a remote
# CI pipeline (except for slower compilation, probably).
#
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#
# `set_tex_cmds` applies to all *latex commands (latex, xelatex, lualatex, ...), so
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# Change default `biber` call, help catch errors faster/clearer. See
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# glstex: generated from glossaries-extra
push @generated_exts, 'loe', 'lol', 'run.xml', 'glg', 'glstex';
# Also delete the *.glstex files from package glossaries-extra. Problem is,
# that that package generates files of the form "basename-digit.glstex" if
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#+TITLE: Optimal and Robust Sensor Fusion #+TITLE: Optimal and Robust Sensor Fusion
:DRAWER: :DRAWER:
#+LATEX_CLASS: IEEEtran #+LATEX_CLASS: IEEEtran
#+LATEX_CLASS_OPTIONS: [conference] #+LATEX_CLASS_OPTIONS: [10pt,final,journal,a4paper]
#+OPTIONS: toc:nil todo:nil #+OPTIONS: toc:nil todo:nil
#+STARTUP: overview #+STARTUP: overview
@ -45,40 +45,44 @@
#+LATEX_HEADER_EXTRA: \usepackage{showframe} #+LATEX_HEADER_EXTRA: \usepackage{showframe}
#+LATEX_HEADER: \def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}} #+LATEX_HEADER: \def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
\bibliographystyle{IEEEtran}
:END: :END:
* LaTeX Config :noexport:
#+begin_src latex :tangle config.tex
#+end_src
* Build :noexport: * Build :noexport:
#+NAME: startblock #+NAME: startblock
#+BEGIN_SRC emacs-lisp :results none #+BEGIN_SRC emacs-lisp :results none
(add-to-list 'org-latex-classes (add-to-list 'org-latex-classes
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"\\documentclass{IEEEtran}" "\\documentclass{IEEEtran}"
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(defun delete-org-comments (backend) ;; Remove automatic org headings
(loop for comment in (reverse (org-element-map (org-element-parse-buffer) (defun my-latex-filter-removeOrgAutoLabels (text backend info)
'comment 'identity)) "Org-mode automatically generates labels for headings despite explicit use of `#+LABEL`. This filter forcibly removes all automatically generated org-labels in headings."
do (when (org-export-derived-backend-p backend 'latex)
(setf (buffer-substring (org-element-property :begin comment) (replace-regexp-in-string "\\\\label{sec:org[a-f0-9]+}\n" "" text)))
(org-element-property :end comment))
"")))
;; add to export hook (add-to-list 'org-export-filter-headline-functions
(add-hook 'org-export-before-processing-hook 'delete-org-comments) 'my-latex-filter-removeOrgAutoLabels)
;; Remove hypersetup ;; Automatic delete org org-comments
(setq org-latex-with-hyperref nil) (defun delete-org-comments (backend)
(loop for comment in (reverse (org-element-map (org-element-parse-buffer)
'comment 'identity))
do
(setf (buffer-substring (org-element-property :begin comment)
(org-element-property :end comment))
"")))
;; add to export hook
(add-hook 'org-export-before-processing-hook 'delete-org-comments)
;; Remove hypersetup
(setq org-latex-with-hyperref nil)
#+END_SRC #+END_SRC
* Abstract :ignore: * Abstract :ignore:
@ -94,6 +98,8 @@
* Introduction * Introduction
<<sec:introduction>> <<sec:introduction>>
cite:mahony08_nonlin_compl_filter_special_orthog_group
- Section ref:sec:optimal_fusion - Section ref:sec:optimal_fusion
- Section ref:sec:robust_fusion - Section ref:sec:robust_fusion
- Section ref:sec:optimal_robust_fusion - Section ref:sec:optimal_robust_fusion
@ -518,4 +524,10 @@ The synthesis objective is to:
* Acknowledgment * Acknowledgment
* Bibliography :ignore: * Bibliography :ignore:
\bibliographystyle{IEEEtran}
\bibliography{ref} \bibliography{ref}
* Local Variables :noexport:
# Local Variables:
# org-latex-packages-alist: nil
# End:

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% Created 2020-10-26 lun. 18:25 % Created 2021-09-10 ven. 10:25
% Intended LaTeX compiler: pdflatex % Intended LaTeX compiler: pdflatex
\documentclass[conference]{IEEEtran} \documentclass[final,journal,a4paper]{IEEEtran}
\usepackage[utf8]{inputenc} \usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc} \usepackage[T1]{fontenc}
\usepackage{graphicx} \usepackage{graphicx}
@ -14,12 +14,6 @@
\usepackage{amssymb} \usepackage{amssymb}
\usepackage{capt-of} \usepackage{capt-of}
\usepackage{hyperref} \usepackage{hyperref}
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\usepackage{bm}
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\usepackage{siunitx}
\IEEEoverridecommandlockouts \IEEEoverridecommandlockouts
\usepackage{cite} \usepackage{cite}
\usepackage{amsmath,amssymb,amsfonts} \usepackage{amsmath,amssymb,amsfonts}
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\def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}} \def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
\usepackage{showframe} \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{Verma Mohit} \IEEEauthorblockA{\textit{BEAMS Department}\\ \textit{Free University of Brussels}, Belgium\\ \textit{Precision Mechatronics Laboratory} \\ \textit{University of Liege}, Belgium \\ mohitverma.serc@csir.res.in }\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 }} \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{Verma Mohit} \IEEEauthorblockA{\textit{BEAMS Department}\\ \textit{Free University of Brussels}, Belgium\\ \textit{Precision Mechatronics Laboratory} \\ \textit{University of Liege}, Belgium \\ mohitverma.serc@csir.res.in }\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{2020-10-26} \date{2021-09-10}
\title{Optimal and Robust Sensor Fusion} \title{Optimal and Robust Sensor Fusion}
\begin{document} \begin{document}
@ -50,9 +44,10 @@ Complementary Filters, Sensor Fusion, H-Infinity Synthesis
\end{IEEEkeywords} \end{IEEEkeywords}
\section{Introduction} \section{Introduction}
\label{sec:org2820158}
\label{sec:introduction} \label{sec:introduction}
\cite{mahony08_nonlin_compl_filter_special_orthog_group}
\begin{itemize} \begin{itemize}
\item Section \ref{sec:optimal_fusion} \item Section \ref{sec:optimal_fusion}
\item Section \ref{sec:robust_fusion} \item Section \ref{sec:robust_fusion}
@ -61,11 +56,9 @@ Complementary Filters, Sensor Fusion, H-Infinity Synthesis
\end{itemize} \end{itemize}
\section{Optimal Super Sensor Noise: \(\mathcal{H}_2\) Synthesis} \section{Optimal Super Sensor Noise: \(\mathcal{H}_2\) Synthesis}
\label{sec:org2513ad9}
\label{sec:optimal_fusion} \label{sec:optimal_fusion}
\subsection{Sensor Model} \subsection{Sensor Model}
\label{sec:orgbcc6cb6}
Let's consider a sensor measuring a physical quantity \(x\) (Figure \ref{fig:sensor_model_noise}). 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 \(G_i(s)\). The sensor has an internal dynamics which is here modelled with a Linear Time Invariant (LTI) system transfer function \(G_i(s)\).
@ -96,19 +89,18 @@ In order to obtain an estimate \(\hat{x}_i\) of \(x\), a model \(\hat{G}_i\) of
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensor_model_noise.pdf} \includegraphics[scale=1,scale=1]{figs/sensor_model_noise.pdf}
\caption{\label{fig:sensor_model_noise}Sensor Model} \caption{\label{fig:sensor_model_noise}Sensor Model}
\end{figure} \end{figure}
\subsection{Sensor Fusion Architecture} \subsection{Sensor Fusion Architecture}
\label{sec:orgdb526ec}
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}). 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. The noise sources \(\tilde{n}_1\) and \(\tilde{n}_2\) are considered to be uncorrelated.
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensor_fusion_noise_arch.pdf} \includegraphics[scale=1,scale=1]{figs/sensor_fusion_noise_arch.pdf}
\caption{\label{fig:sensor_fusion_noise_arch}Sensor Fusion Architecture with sensor noise} \caption{\label{fig:sensor_fusion_noise_arch}Sensor Fusion Architecture with sensor noise}
\end{figure} \end{figure}
@ -138,7 +130,6 @@ In such case, the super sensor estimate \(\hat{x}\) is equal to \(x\) plus the n
\end{equation} \end{equation}
\subsection{Super Sensor Noise} \subsection{Super Sensor Noise}
\label{sec:org48c0d52}
Let's note \(n\) the super sensor noise. Let's note \(n\) the super sensor noise.
\begin{equation} \begin{equation}
n = \left( H_1 N_1 \right) \tilde{n}_1 + \left( H_2 N_2 \right) \tilde{n}_2 n = \left( H_1 N_1 \right) \tilde{n}_1 + \left( H_2 N_2 \right) \tilde{n}_2
@ -152,7 +143,6 @@ 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. 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} \subsection{\(\mathcal{H}_2\) Synthesis of Complementary Filters}
\label{sec:org0d9384e}
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 \(\hat{x}\). 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 \(n\): And the goal is the minimize the Root Mean Square (RMS) value of \(n\):
@ -191,67 +181,63 @@ We then have that the \(\mathcal{H}_2\) synthesis applied on \(P_{\mathcal{H}_2}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/h_two_optimal_fusion.pdf} \includegraphics[scale=1,scale=1]{figs/h_two_optimal_fusion.pdf}
\caption{\label{fig:h_two_optimal_fusion}Generalized plant \(P_{\mathcal{H}_2}\) used for the \(\mathcal{H}_2\) synthesis of complementary filters} \caption{\label{fig:h_two_optimal_fusion}Generalized plant \(P_{\mathcal{H}_2}\) used for the \(\mathcal{H}_2\) synthesis of complementary filters}
\end{figure} \end{figure}
\subsection{Example} \subsection{Example}
\label{sec:org99002de}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensors_nominal_dynamics.pdf} \includegraphics[scale=1,scale=1]{figs/sensors_nominal_dynamics.pdf}
\caption{\label{fig:sensors_nominal_dynamics}Sensor nominal dynamics from the velocity of the object to the output voltage} \caption{\label{fig:sensors_nominal_dynamics}Sensor nominal dynamics from the velocity of the object to the output voltage}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensors_noise.pdf} \includegraphics[scale=1,scale=1]{figs/sensors_noise.pdf}
\caption{\label{fig:sensors_noise}Amplitude spectral density of the sensors \(\sqrt{\Phi_{n_i}(\omega)} = |N_i(j\omega)|\)} \caption{\label{fig:sensors_noise}Amplitude spectral density of the sensors \(\sqrt{\Phi_{n_i}(\omega)} = |N_i(j\omega)|\)}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/htwo_comp_filters.pdf} \includegraphics[scale=1,scale=1]{figs/htwo_comp_filters.pdf}
\caption{\label{fig:htwo_comp_filters}Obtained complementary filters using the \(\mathcal{H}_2\) Synthesis} \caption{\label{fig:htwo_comp_filters}Obtained complementary filters using the \(\mathcal{H}_2\) Synthesis}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/psd_sensors_htwo_synthesis.pdf} \includegraphics[scale=1,scale=1]{figs/psd_sensors_htwo_synthesis.pdf}
\caption{\label{fig:psd_sensors_htwo_synthesis}Power Spectral Density of the estimated \(\hat{x}\) using the two sensors alone and using the optimally fused signal} \caption{\label{fig:psd_sensors_htwo_synthesis}Power Spectral Density of the estimated \(\hat{x}\) using the two sensors alone and using the optimally fused signal}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/super_sensor_time_domain_h2.pdf} \includegraphics[scale=1,scale=1]{figs/super_sensor_time_domain_h2.pdf}
\caption{\label{fig:super_sensor_time_domain_h2}Noise of individual sensors and noise of the super sensor} \caption{\label{fig:super_sensor_time_domain_h2}Noise of individual sensors and noise of the super sensor}
\end{figure} \end{figure}
\subsection{Robustness Problem} \subsection{Robustness Problem}
\label{sec:org262893f}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensors_nominal_dynamics_and_uncertainty.pdf} \includegraphics[scale=1,scale=1]{figs/sensors_nominal_dynamics_and_uncertainty.pdf}
\caption{\label{fig:sensors_nominal_dynamics_and_uncertainty}Nominal Sensor Dynamics \(\hat{G}_i\) (solid lines) as well as the spread of the dynamical uncertainty (background color)} \caption{\label{fig:sensors_nominal_dynamics_and_uncertainty}Nominal Sensor Dynamics \(\hat{G}_i\) (solid lines) as well as the spread of the dynamical uncertainty (background color)}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/super_sensor_dynamical_uncertainty_H2.pdf} \includegraphics[scale=1,scale=1]{figs/super_sensor_dynamical_uncertainty_H2.pdf}
\caption{\label{fig:super_sensor_dynamical_uncertainty_H2}Super sensor dynamical uncertainty when using the \(\mathcal{H}_2\) Synthesis} \caption{\label{fig:super_sensor_dynamical_uncertainty_H2}Super sensor dynamical uncertainty when using the \(\mathcal{H}_2\) Synthesis}
\end{figure} \end{figure}
\section{Robust Sensor Fusion: \(\mathcal{H}_\infty\) Synthesis} \section{Robust Sensor Fusion: \(\mathcal{H}_\infty\) Synthesis}
\label{sec:org7c9047e}
\label{sec:robust_fusion} \label{sec:robust_fusion}
\subsection{Representation of Sensor Dynamical Uncertainty} \subsection{Representation of Sensor Dynamical Uncertainty}
\label{sec:org7bd4379}
In Section \ref{sec:optimal_fusion}, the model \(\hat{G}_i(s)\) of the sensor was considered to be perfect. 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. In reality, there are always uncertainty (neglected dynamics) associated with the estimation of the sensor dynamics.
@ -266,12 +252,11 @@ The sensor can then be represented as shown in Figure \ref{fig:sensor_model_unce
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensor_model_uncertainty.pdf} \includegraphics[scale=1,scale=1]{figs/sensor_model_uncertainty.pdf}
\caption{\label{fig:sensor_model_uncertainty}Sensor Model including Dynamical Uncertainty} \caption{\label{fig:sensor_model_uncertainty}Sensor Model including Dynamical Uncertainty}
\end{figure} \end{figure}
\subsection{Sensor Fusion Architecture} \subsection{Sensor Fusion Architecture}
\label{sec:org0b8ce2b}
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. 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: The super sensor estimate is then:
@ -291,12 +276,11 @@ As \(H_1\) and \(H_2\) are complementary filters, we finally have:
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensor_fusion_arch_uncertainty.pdf} \includegraphics[scale=1,scale=1]{figs/sensor_fusion_arch_uncertainty.pdf}
\caption{\label{fig:sensor_fusion_arch_uncertainty}Sensor Fusion Architecture with sensor model uncertainty} \caption{\label{fig:sensor_fusion_arch_uncertainty}Sensor Fusion Architecture with sensor model uncertainty}
\end{figure} \end{figure}
\subsection{Super Sensor Dynamical Uncertainty} \subsection{Super Sensor Dynamical Uncertainty}
\label{sec:org725af92}
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}. 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}.
@ -304,14 +288,13 @@ And we can see that the dynamical uncertainty of the super sensor is equal to th
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/uncertainty_set_super_sensor.pdf} \includegraphics[scale=1,scale=1]{figs/uncertainty_set_super_sensor.pdf}
\caption{\label{fig:uncertainty_set_super_sensor}Super Sensor model uncertainty displayed in the complex plane} \caption{\label{fig:uncertainty_set_super_sensor}Super Sensor model uncertainty displayed in the complex plane}
\end{figure} \end{figure}
At frequencies where \(\left|W_i(j\omega)\right| > 1\) the uncertainty exceeds \(100\%\) and sensor fusion is impossible. 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} \subsection{\(\mathcal{H_\infty}\) Synthesis of Complementary Filters}
\label{sec:org941ed72}
In order for the fusion to be ``robust'', meaning no phase drop will be induced in the super sensor dynamics, 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. 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.
@ -352,51 +335,48 @@ The \(\mathcal{H}_\infty\) norm of Eq. \eqref{eq:Hinf_norm} is equals to \(\sigm
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/h_infinity_robust_fusion.pdf} \includegraphics[scale=1,scale=1]{figs/h_infinity_robust_fusion.pdf}
\caption{\label{fig:h_infinity_robust_fusion}Generalized plant \(P_{\mathcal{H}_\infty}\) used for the \(\mathcal{H}_\infty\) synthesis of complementary filters} \caption{\label{fig:h_infinity_robust_fusion}Generalized plant \(P_{\mathcal{H}_\infty}\) used for the \(\mathcal{H}_\infty\) synthesis of complementary filters}
\end{figure} \end{figure}
\subsection{Example} \subsection{Example}
\label{sec:org7df520f}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensors_uncertainty_weights.pdf} \includegraphics[scale=1,scale=1]{figs/sensors_uncertainty_weights.pdf}
\caption{\label{fig:sensors_uncertainty_weights}Magnitude of the multiplicative uncertainty weights \(|W_i(j\omega)|\)} \caption{\label{fig:sensors_uncertainty_weights}Magnitude of the multiplicative uncertainty weights \(|W_i(j\omega)|\)}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/weight_uncertainty_bounds_Wu.pdf} \includegraphics[scale=1,scale=1]{figs/weight_uncertainty_bounds_Wu.pdf}
\caption{\label{fig:weight_uncertainty_bounds_Wu}Uncertainty region of the two sensors as well as the wanted maximum uncertainty of the super sensor (dashed lines)} \caption{\label{fig:weight_uncertainty_bounds_Wu}Uncertainty region of the two sensors as well as the wanted maximum uncertainty of the super sensor (dashed lines)}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/hinf_comp_filters.pdf} \includegraphics[scale=1,scale=1]{figs/hinf_comp_filters.pdf}
\caption{\label{fig:hinf_comp_filters}Obtained complementary filters using the \(\mathcal{H}_\infty\) Synthesis} \caption{\label{fig:hinf_comp_filters}Obtained complementary filters using the \(\mathcal{H}_\infty\) Synthesis}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/super_sensor_dynamical_uncertainty_Hinf.pdf} \includegraphics[scale=1,scale=1]{figs/super_sensor_dynamical_uncertainty_Hinf.pdf}
\caption{\label{fig:super_sensor_dynamical_uncertainty_Hinf}Super sensor dynamical uncertainty (solid curve) when using the \(\mathcal{H}_\infty\) Synthesis} \caption{\label{fig:super_sensor_dynamical_uncertainty_Hinf}Super sensor dynamical uncertainty (solid curve) when using the \(\mathcal{H}_\infty\) Synthesis}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/psd_sensors_hinf_synthesis.pdf} \includegraphics[scale=1,scale=1]{figs/psd_sensors_hinf_synthesis.pdf}
\caption{\label{fig:psd_sensors_hinf_synthesis}Power Spectral Density of the estimated \(\hat{x}\) using the two sensors alone and using the \(\mathcal{H}_\infty\) synthesis} \caption{\label{fig:psd_sensors_hinf_synthesis}Power Spectral Density of the estimated \(\hat{x}\) using the two sensors alone and using the \(\mathcal{H}_\infty\) synthesis}
\end{figure} \end{figure}
\section{Optimal and Robust Sensor Fusion: Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis} \section{Optimal and Robust Sensor Fusion: Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
\label{sec:org75a038a}
\label{sec:optimal_robust_fusion} \label{sec:optimal_robust_fusion}
\subsection{Sensor with noise and model uncertainty} \subsection{Sensor with noise and model uncertainty}
\label{sec:org3810d6b}
We wish now to combine the two previous synthesis, that is to say 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 \(N_i(s)\). The sensors are now modelled by a white noise with unitary PSD \(\tilde{n}_i\) shaped by a LTI transfer function \(N_i(s)\).
@ -412,12 +392,11 @@ Multiplying by the inverse of the nominal model of the sensor dynamics gives an
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensor_model_noise_uncertainty.pdf} \includegraphics[scale=1,scale=1]{figs/sensor_model_noise_uncertainty.pdf}
\caption{\label{fig:sensor_model_noise_uncertainty}Sensor Model including Noise and Dynamical Uncertainty} \caption{\label{fig:sensor_model_noise_uncertainty}Sensor Model including Noise and Dynamical Uncertainty}
\end{figure} \end{figure}
\subsection{Sensor Fusion Architecture} \subsection{Sensor Fusion Architecture}
\label{sec:org3758b1e}
For reason of space, the blocks \(\hat{G}_i\) and \(\hat{G}_i^{-1}\) are omitted. For reason of space, the blocks \(\hat{G}_i\) and \(\hat{G}_i^{-1}\) are omitted.
@ -439,12 +418,11 @@ The estimate \(\hat{x}\) of \(x\)
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/sensor_fusion_arch_full.pdf} \includegraphics[scale=1,scale=1]{figs/sensor_fusion_arch_full.pdf}
\caption{\label{fig:sensor_fusion_arch_full}Super Sensor Fusion with both sensor noise and sensor model uncertainty} \caption{\label{fig:sensor_fusion_arch_full}Super Sensor Fusion with both sensor noise and sensor model uncertainty}
\end{figure} \end{figure}
\subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis} \subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
\label{sec:org06317f4}
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. 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.
@ -474,59 +452,52 @@ The synthesis objective is to:
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/mixed_h2_hinf_synthesis.pdf} \includegraphics[scale=1,scale=1]{figs/mixed_h2_hinf_synthesis.pdf}
\caption{\label{fig:mixed_h2_hinf_synthesis}Generalized plant \(P_{\mathcal{H}_2/\matlcal{H}_\infty}\) used for the mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis of complementary filters} \caption{\label{fig:mixed_h2_hinf_synthesis}Generalized plant \(P_{\mathcal{H}_2/\matlcal{H}_\infty}\) used for the mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis of complementary filters}
\end{figure} \end{figure}
\subsection{Example} \subsection{Example}
\label{sec:org42ee165}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/htwo_hinf_comp_filters.pdf} \includegraphics[scale=1,scale=1]{figs/htwo_hinf_comp_filters.pdf}
\caption{\label{fig:htwo_hinf_comp_filters}Obtained complementary filters after mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis} \caption{\label{fig:htwo_hinf_comp_filters}Obtained complementary filters after mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/psd_sensors_htwo_hinf_synthesis.pdf} \includegraphics[scale=1,scale=1]{figs/psd_sensors_htwo_hinf_synthesis.pdf}
\caption{\label{fig:psd_sensors_htwo_hinf_synthesis}Power Spectral Density of the Super Sensor obtained with the mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis} \caption{\label{fig:psd_sensors_htwo_hinf_synthesis}Power Spectral Density of the Super Sensor obtained with the mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/super_sensor_time_domain_h2_hinf.pdf} \includegraphics[scale=1,scale=1]{figs/super_sensor_time_domain_h2_hinf.pdf}
\caption{\label{fig:super_sensor_time_domain_h2_hinf}Noise of individual sensors and noise of the super sensor} \caption{\label{fig:super_sensor_time_domain_h2_hinf}Noise of individual sensors and noise of the super sensor}
\end{figure} \end{figure}
\begin{figure}[htbp] \begin{figure}[htbp]
\centering \centering
\includegraphics[scale=1]{figs/super_sensor_dynamical_uncertainty_Htwo_Hinf.pdf} \includegraphics[scale=1,scale=1]{figs/super_sensor_dynamical_uncertainty_Htwo_Hinf.pdf}
\caption{\label{fig:super_sensor_dynamical_uncertainty_Htwo_Hinf}Super sensor dynamical uncertainty (solid curve) when using the mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis} \caption{\label{fig:super_sensor_dynamical_uncertainty_Htwo_Hinf}Super sensor dynamical uncertainty (solid curve) when using the mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
\end{figure} \end{figure}
\section{Experimental Validation} \section{Experimental Validation}
\label{sec:orge381a2a}
\label{sec:experimental_validation} \label{sec:experimental_validation}
\subsection{Experimental Setup} \subsection{Experimental Setup}
\label{sec:org473ab00}
\subsection{Sensor Noise and Dynamical Uncertainty} \subsection{Sensor Noise and Dynamical Uncertainty}
\label{sec:orgebcb65d}
\subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis} \subsection{Mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis}
\label{sec:org0259d19}
\subsection{Super Sensor Noise and Dynamical Uncertainty} \subsection{Super Sensor Noise and Dynamical Uncertainty}
\label{sec:orgdb5d29f}
\section{Conclusion} \section{Conclusion}
\label{sec:org07df454}
\label{sec:conclusion} \label{sec:conclusion}
\section{Acknowledgment} \section{Acknowledgment}
\label{sec:org7b7e461}
\bibliographystyle{IEEEtran}
\bibliography{ref} \bibliography{ref}
\end{document} \end{document}

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@ -1,28 +0,0 @@
% H Infini
\newcommand{\hinf}{\mathcal{H}_\infty}
% H 2
\newcommand{\htwo}{\mathcal{H}_2}
% Omega
\newcommand{\w}{\omega}
% H-Infinity Norm
\newcommand{\hnorm}[1]{\left\|#1\right\|_{\infty}}
% H-2 Norm
\newcommand{\normtwo}[1]{\left\|#1\right\|_{2}}
% Norm
\newcommand{\norm}[1]{\left\|#1\right\|}
% Absolute value
\newcommand{\abs}[1]{\left\lvert #1 \right\lvert}
% Minimum Subscript
\newcommand{\smin}{_{\text{min}}}
% Maximum Subscript
\newcommand{\smax}{_{\text{max}}}
\newcommand*\colvec[1]{\begin{bmatrix}#1\end{bmatrix}}

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