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#+TITLE: Robust and Optimal Sensor Fusion
:DRAWER:
#+LATEX_CLASS: IEEEtran
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#+STARTUP: overview
#+DATE: {{{time(%Y-%m-%d)}}}
#+AUTHOR: @@latex:\IEEEauthorblockN{Dehaeze Thomas}@@
#+AUTHOR: @@latex:\IEEEauthorblockA{\textit{European Synchrotron Radiation Facility} \\@@
#+AUTHOR: @@latex:Grenoble, France\\@@
#+AUTHOR: @@latex:\textit{Precision Mechatronics Laboratory} \\@@
#+AUTHOR: @@latex:\textit{University of Liege}, Belgium \\@@
#+AUTHOR: @@latex:thomas.dehaeze@esrf.fr@@
#+AUTHOR: @@latex:}\and@@
#+AUTHOR: @@latex:\IEEEauthorblockN{Collette Christophe}@@
#+AUTHOR: @@latex:\IEEEauthorblockA{\textit{BEAMS Department}\\@@
#+AUTHOR: @@latex:\textit{Free University of Brussels}, Belgium\\@@
#+AUTHOR: @@latex:\textit{Precision Mechatronics Laboratory} \\@@
#+AUTHOR: @@latex:\textit{University of Liege}, Belgium \\@@
#+AUTHOR: @@latex:ccollett@ulb.ac.be@@
#+AUTHOR: @@latex:}@@
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* LaTeX Config :noexport:
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* Abstract :ignore:
#+begin_abstract
Abstract text to be done
#+end_abstract
* Keywords :ignore:
#+begin_IEEEkeywords
Complementary Filters, Sensor Fusion, H-Infinity Synthesis
#+end_IEEEkeywords
* Introduction
<<sec:introduction>>
* Optimal Super Sensor Noise: $\mathcal{H}_2$ Synthesis
<<sec:optimal_fusion>>
** Sensor Model
** Sensor Fusion Architecture
#+name: fig:sensor_fusion_noise_arch
#+caption: Figure caption
#+attr_latex: :scale 1
[[file:figs/sensor_fusion_noise_arch.pdf]]
Let note $\Phi$ the PSD.
$\tilde{n}_1$ and $\tilde{n}_2$ are white noise with unitary power spectral density:
\begin{equation}
\Phi_{\tilde{n}_i}(\omega) = 1
\end{equation}
\begin{equation}
\hat{x} = \left( H_1 \hat{G}_1^{-1} G_1 + H_2 \hat{G}_2^{-1} G_2 \right) x + \left( H_1 \hat{G}_1^{-1} N_1 \right) \tilde{n}_1 + \left( H_2 \hat{G}_2^{-1} N_2 \right) \tilde{n}_2
\end{equation}
Suppose the sensor dynamical model $\hat{G}_i$ is perfect:
\begin{equation}
\hat{G}_i = G_i
\end{equation}
Complementary Filters
\begin{equation}
H_1(s) + H_2(s) = 1
\end{equation}
\begin{equation}
\hat{x} = x + \left( H_1 \hat{G}_1^{-1} N_1 \right) \tilde{n}_1 + \left( H_2 \hat{G}_2^{-1} N_2 \right) \tilde{n}_2
\end{equation}
Perfect dynamics + filter noise
** Super Sensor Noise
Let's note $n$ the super sensor noise.
Its PSD is determined by:
\begin{equation}
\Phi_n(\omega) = \left| H_1 \hat{G}_1^{-1} N_1 \right|^2 + \left| H_2 \hat{G}_2^{-1} N_2 \right|^2
\end{equation}
** $\mathcal{H}_2$ Synthesis of Complementary Filters
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 $n$:
#+name: eq:rms_value_estimation
\begin{equation}
\sigma_{n} = \sqrt{\int_0^\infty \Phi_{\hat{n}}(\omega) d\omega} = \left\| \begin{matrix} \hat{G}_1^{-1} N_1 H_1 \\ \hat{G}_2^{-1} N_2 H_2 \end{matrix} \right\|_2
\end{equation}
Thus, the goal is to design $H_1(s)$ and $H_2(s)$ such that $H_1(s) + H_2(s) = 1$ and such that $\left\| \begin{matrix} \hat{G}_1^{-1} N_1 H_1 \\ \hat{G}_2^{-1} N_2 H_2 \end{matrix} \right\|_2$ is minimized.
\begin{equation}
\begin{pmatrix}
z_1 \\ z_2 \\ v
\end{pmatrix} = \begin{bmatrix}
\hat{G}_1^{-1} N_1 & -\hat{G}_1^{-1} N_1 \\
0 & \hat{G}_2^{-1} N_2 \\
1 & 0
\end{bmatrix} \begin{pmatrix}
w \\ u
\end{pmatrix}
\end{equation}
The $\mathcal{H}_2$ synthesis of the complementary filters thus minimized the RMS value of the super sensor noise.
#+name: fig:h_two_optimal_fusion
#+caption: Figure caption
#+attr_latex: :scale 1
[[file:figs/h_two_optimal_fusion.pdf]]
** Example
** Robustness Problem
* Robust Sensor Fusion: $\mathcal{H}_\infty$ Synthesis
<<sec:robust_fusion>>
** Representation of Sensor Dynamical Uncertainty
** Sensor Fusion Architecture
\begin{equation}
\hat{x} = \left( H_1 \hat{G}_1^{-1} (1 + w_1 \Delta_1) G_1 + H_2 \hat{G}_2^{-1} (1 + w_2 \Delta_2) G_2 \right) x
\end{equation}
with $\Delta_i$ is any transfer function satisfying $\| \Delta_i \|_\infty < 1$.
Suppose the model inversion is equal to the nominal model:
\begin{equation}
\hat{G}_i = G_i
\end{equation}
\begin{equation}
\hat{x} = \left( 1 + H_1 w_1 \Delta_1 + H_2 w_2 \Delta_2 \right) x
\end{equation}
#+name: fig:sensor_fusion_arch_uncertainty
#+caption: Figure caption
#+attr_latex: :scale 1
[[file:figs/sensor_fusion_arch_uncertainty.pdf]]
** Super Sensor Dynamical Uncertainty
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 $|w_1(j\omega) H_1(j\omega)| + |w_2(j\omega) H_2(j\omega)|$.
#+name: fig:uncertainty_set_super_sensor
#+caption: Figure caption
#+attr_latex: :scale 1
[[file:figs/uncertainty_set_super_sensor.pdf]]
** $\mathcal{H_\infty}$ Synthesis of Complementary Filters
In order to minimize the super sensor dynamical uncertainty
#+name: fig:h_infinity_robust_fusion
#+caption: Figure caption
#+attr_latex: :scale 1
[[file:figs/h_infinity_robust_fusion.pdf]]
** Example
* Optimal and Robust Sensor Fusion: Mixed $\mathcal{H}_2/\mathcal{H}_\infty$ Synthesis
<<sec:optimal_robust_fusion>>
** Sensor Fusion Architecture
#+name: fig:sensor_fusion_arch_full
#+caption: Figure caption
#+attr_latex: :scale 1
[[file:figs/sensor_fusion_arch_full.pdf]]
** Synthesis Objective
** Mixed $\mathcal{H}_2/\mathcal{H}_\infty$ Synthesis
#+name: fig:mixed_h2_hinf_synthesis
#+caption: Figure caption
#+attr_latex: :scale 1
[[file:figs/mixed_h2_hinf_synthesis.pdf]]
** Example
* Experimental Validation
<<sec:experimental_validation>>
** Experimental Setup
** Sensor Noise and Dynamical Uncertainty
** Mixed $\mathcal{H}_2/\mathcal{H}_\infty$ Synthesis
** Super Sensor Noise and Dynamical Uncertainty
* Conclusion
<<sec:conclusion>>
* Acknowledgment
* Bibliography :ignore:
\bibliography{ref}