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set_tex_cmds("--shell-escape -interaction=nonstopmode --synctex=1 %O %S");
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#+TITLE: Optimal and Robust Sensor Fusion
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#+TITLE: Optimal and Robust Sensor Fusion
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#+LATEX_HEADER: \def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
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("\\subparagraph{%s}" . "\\subparagraph*{%s}"))
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;; Remove automatic org headings
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(defun my-latex-filter-removeOrgAutoLabels (text backend info)
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"Org-mode automatically generates labels for headings despite explicit use of `#+LABEL`. This filter forcibly removes all automatically generated org-labels in headings."
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(when (org-export-derived-backend-p backend 'latex)
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;; Automatic delete org org-comments
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(defun delete-org-comments (backend)
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(loop for comment in (reverse (org-element-map (org-element-parse-buffer)
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(loop for comment in (reverse (org-element-map (org-element-parse-buffer)
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'comment 'identity))
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do
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do
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(org-element-property :end comment))
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;; add to export hook
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;; Remove hypersetup
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;; Remove hypersetup
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(setq org-latex-with-hyperref nil)
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* Abstract :ignore:
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* Abstract :ignore:
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* Introduction
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* Introduction
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||||||
<<sec:introduction>>
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<<sec:introduction>>
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||||||
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|
||||||
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cite:mahony08_nonlin_compl_filter_special_orthog_group
|
||||||
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||||||
- Section ref:sec:optimal_fusion
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- Section ref:sec:optimal_fusion
|
||||||
- Section ref:sec:robust_fusion
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- Section ref:sec:robust_fusion
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- Section ref:sec:optimal_robust_fusion
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- Section ref:sec:optimal_robust_fusion
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@ -518,4 +524,10 @@ The synthesis objective is to:
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* Acknowledgment
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* Acknowledgment
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* Bibliography :ignore:
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* Bibliography :ignore:
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\bibliographystyle{IEEEtran}
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# End:
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@ -1,6 +1,6 @@
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% Created 2020-10-26 lun. 18:25
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% Created 2021-09-10 ven. 10:25
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% Intended LaTeX compiler: pdflatex
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\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}
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||||||
\title{Optimal and Robust Sensor Fusion}
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\title{Optimal and Robust Sensor Fusion}
|
||||||
\begin{document}
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\begin{document}
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||||||
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|
||||||
@ -50,9 +44,10 @@ Complementary Filters, Sensor Fusion, H-Infinity Synthesis
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|||||||
\end{IEEEkeywords}
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\end{IEEEkeywords}
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||||||
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||||||
\section{Introduction}
|
\section{Introduction}
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||||||
\label{sec:org2820158}
|
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||||||
\label{sec:introduction}
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\label{sec:introduction}
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||||||
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||||||
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\cite{mahony08_nonlin_compl_filter_special_orthog_group}
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||||||
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||||||
\begin{itemize}
|
\begin{itemize}
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||||||
\item Section \ref{sec:optimal_fusion}
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\item Section \ref{sec:optimal_fusion}
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||||||
\item Section \ref{sec:robust_fusion}
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\item Section \ref{sec:robust_fusion}
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||||||
@ -61,11 +56,9 @@ Complementary Filters, Sensor Fusion, H-Infinity Synthesis
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|||||||
\end{itemize}
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\end{itemize}
|
||||||
|
|
||||||
\section{Optimal Super Sensor Noise: \(\mathcal{H}_2\) Synthesis}
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\section{Optimal Super Sensor Noise: \(\mathcal{H}_2\) Synthesis}
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||||||
\label{sec:org2513ad9}
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|
||||||
\label{sec:optimal_fusion}
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\label{sec:optimal_fusion}
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||||||
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|
||||||
\subsection{Sensor Model}
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\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 \(G_i(s)\).
|
||||||
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||||||
@ -96,19 +89,18 @@ In order to obtain an estimate \(\hat{x}_i\) of \(x\), a model \(\hat{G}_i\) of
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|||||||
|
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||||||
\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}
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||||||
\caption{\label{fig:sensor_model_noise}Sensor Model}
|
\caption{\label{fig:sensor_model_noise}Sensor Model}
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||||||
\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 @@
|
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% 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}}
|
|