Template to write solutions to homework assignments that involve math equations, figures and code. Just one line command to add the Matlab code with its file name.
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\begin{document}
%Header-Make sure you update this information!!!!
\noindent
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\large\textbf{Venkatraman Renganathan} \hfill \textbf{Homework - \#} \\
Email: veralevel@gethu.edu \hfill ID: 123456789 \\
\normalsize Course: MECH 6325 - Optimal Estimation \& Kalman Filtering \hfill Term: Fall 2019\\
Instructor: Dr. Sriram \hfill Due Date: $22^{nd}$ November, 2019 \\
\noindent\rule{7in}{2.8pt}
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% Problem 1
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\begin{problem}{1}
Consider the scalar system
\begin{align*}
\Dot{x} &= -x + u + w
\end{align*}
$w$ is zero-mean process noise with a variance of $Q$. The control has a mean value of $u_0$, an uncertainty of $2$ (one standard deviation), and is uncorrelated with $w$. Rewrite the system equations to obtain an equivalent system with a normalized control that is perfectly known. What is the variance of the new process noise term in the transformed system equation?
\end{problem}
\begin{solution}
The variance of the new process noise, $w_u$ is $\Sigma_{w_{u}} = Q + \sigma^2_u = Q + 4$.
\begin{align*}
\Dot{x} &= -x + u_0 + \underbrace{w + \Delta u}_{w_{u}}, \quad w_u \sim (0, Q + \sigma^2_u).
\end{align*}
\end{solution}
\noindent\rule{7in}{2.8pt}
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% Problem 2
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\begin{problem}{2}
Consider the system
\begin{align*}
x_{k+1} &= \phi x_{k} + w_{k}, \\
y_k &= x_k,
\end{align*}
where $w_k \sim (0, 1)$, and $\phi = 0.9$ is an unknown constant. Design an extended Kalman filter to estimate $\phi$. Simulate the filter for $100$ time steps with $x_0 = 1, P_0 = I , \hat{x}_{0} = 0$, and $\hat{\phi}_{0} = 0$. Hand in your source code and a plot showing $\hat{\phi}$ as a function of time.
\end{problem}
\begin{solution}
Perform the measurement update of the state estimate and estimation error covariance as follows
\begin{align*}
K_k &= P^{-}_k H^{\top}_k (H_k P^{-}_k H^{\top}_k + R_k)^{-1} = P^{-}_k H^{\top}_k (H_k P^{-}_k H^{\top}_k)^{-1}, \quad \text{Since }R_k = 0, \\
\hat{\bar{x}}^{+}_{k} &= \hat{\bar{x}}^{-}_{k} + K_k (y_k - h_k(\hat{\bar{x}}^{-}_{k}, 0)) \\
&= \hat{\bar{x}}^{-}_{k} + K_k (y_k - \hat{x}^{-}_{k}), \quad \text{Since } \hat{\phi}^{-}_{k} = 0, \\
P^{+}_k &= (I - K_k H_k) P^{-}_k
\end{align*}
\begin{figure}[H]
\centering
\includegraphics[scale=0.25]{q2.png}
\caption{Plot showing $\hat{\phi}$ as a function of time.}
\label{fig_q2l}
\end{figure}
\end{solution}
\lstinputlisting{HW6Q2.m}
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\end{document}