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\title{Lecture Notes:  Noises}
\author{}
\date{}

\maketitle

The problem of tracking something can be stated as a question of
``where is it?''
As figure \ref{scalar} demonstrates, the answer to this question
can be given as a scalar (``it is at 15.2'').
This provides useful information for making decisions.
For example, in the case of tracking an enemy plane,
this provides a location at which to aim a weapon.

\begin{figure}
\begin{center}
\includegraphics[width=3.0in]{scalar.eps}
\end{center}
\caption{The scalar answer to ``you are here''.}
\label{scalar}
\end{figure}

But the reality in a tracking problem is that the answer is rarely
if ever certain.
A filter makes this explicit by calculating a probability distribution
for each variable of interest, rather than a scalar.
As figure \ref{prob-dist} illustrates,
``you are here'' on a map can be reimagined as ``you are
likely somewhere in this area, according to this probability curve''.
(It takes a lot more room to say this on a map,
which could be why ``you are here'' is more common on mall maps.
Or maybe mall-goers just don't like math.)

\begin{figure}
\begin{center}
\includegraphics[width=3.0in]{prob-dist.eps}
\end{center}
\caption{The probability distribution answer to ``you are here''.
In a filtering problem, a probability distribution is associated
with every variable of interest.}
\label{prob-dist}
\end{figure}

In the context of filtering, there are two types of noises
that are modeled using probability distributions.
{\bf Dynamic noise} (also called system noise)
refers to the uncertainty in predictions in
the state transition equations.
For example, consider the equations we used previously to model
the the 1D motion of an object moving along an $x$ axis.
The state variables are $[x_t,\dot{x_t}]$, where $x_t$ provides
the position of the object and $\dot{x_t}$ provides the velocity of
the object at time $t$.
For the state transition equations we wrote:
\begin{eqnarray}
x_{t+1,t} & = & x_{t,t} + \dot{x}_{t,t} T \\
\dot{x}_{t+1,t} & = & \dot{x}_{t,t}
\end{eqnarray}
where $T$ is the interval of time between sensor readings.
These equations assume that the object is moving at a constant velocity.
However, suppose that the object can have a non-zero acceleration.
This can be written into the equations as follows:
\begin{eqnarray}
x_{t+1,t} & = & x_{t,t} + \dot{x}_{t,t} T \label{dynamic noise} \\
\dot{x}_{t+1,t} & = & \dot{x}_{t,t} + N(0,\sigma^2_{a})
\end{eqnarray}
where $N(0,\sigma^2_{a})$ denotes a random, normally distributed
variable with a mean of zero and a standard deviation of $\sigma_{a}$.
The size of $\sigma_a$ defines how large of an acceleration can be
expected during each prediction interval.

At least one of the predicted state variables must be affected
by a dynamic noise, but this is not required of all predicted
state variables.
For this example, it would not make sense to have a dynamic noise on
the position variable (unless the object can teleport).
It is better to leave the prediction uncertainty in velocity,
which has a real-world interpretation.
If no state variables have dynamic noise, then there is no reason
to filter, because the thing being tracked has no uncertainty
in its behavior.

{\bf Measurement noise} refers to the
uncertainty in the sensor readings.
For our 1D example, we previously wrote the measurement equation as:
\begin{equation}
y_t  =  x_{t,t}
\end{equation}
This equation states that we observe the position of the object
along the x axis with no uncertainty.
However, suppose that the measurements are corrupted by noise.
This can be written into the measurement equation as:
\begin{equation}
y_t  =  x_{t,t} + N(0,\sigma^2_n)
\end{equation}
where $N(0,\sigma^2_{n})$ denotes a random, normally distributed
variable with a mean of zero and a standard deviation of $\sigma_{n}$.
The size of $\sigma_n$ defines the amount of expected corruption
in a measurement.

As with dynamic noise, at least one measurement variable (but not
necessarily all of them) should be affected by measurement noise.
If there is no measurement noise, then the only reason to filter
would be to model the behavior between measurements.

Previously, we wrote the filtering update equations for the
1D example as follows:
\begin{eqnarray}
x_{t,t} & = & x_{t,t-1} + g_t (y_t - x_{t,t-1}) \label{update_eq} \\
\dot{x}_{t,t} & = & \dot{x}_{t,t-1} + h_t \frac{y_t - x_{t,t-1}}{T}
\label{velocity_update}
\end{eqnarray}
In an example iteration, we obtained a sensor reading that differed
from the prediction.
The values $g_t$ and $h_t$ were introduced as weights to control
how to combine the estimates.
Using the concepts of dynamic noise and measurement noise, we can
now develop a mathematical formula for how to calculate values
for these weights.  The basic idea is to balance the combination
depending on the relative magnitude of the noises.
For example, if the measurement noise is much higher than the
dynamic noise, we would use relatively low values for $g_t$ and
$h_t$, relying more upon the predictions.
Conversely, if the dynamic noise was much higher than the
measurement noise, we would use higher values for $g_t$ and $h_t$,
which puts more weight on the measurements.

The Kalman filter takes the following approach to the problem.
Assume that we have two estimates of a quantity, and we are seeking
the best linear combination of those estimates:
\begin{equation}
c = K_1 x + K_2 y
\end{equation}
where $x$ and $y$ are the estimates and $c$ is the combined estimate.
How should the constants $K_1$ and $K_2$ be chosen?
Assume that each estimate has a known variance, as illustrated
in figure \ref{combine}.
\begin{figure}
\begin{center}
\includegraphics[width=3.0in]{combine.eps}
\end{center}
\caption{Given two estimates x and y of an unknown c, they
can be combined by weighting according to their variances.}
\label{combine}
\end{figure}
If we believe the estimates according to the inverse of the size
of these variances, we can define the error of the combined estimate as:
\begin{equation}
E = \frac{(x-c)^2}{\sigma^2_x} + \frac{(y-c)^2}{\sigma^2_y}
\end{equation}
This makes intuitive sense.  If the variance of the estimate $x$ is small,
then we require $x$ to be very close to the actual value $c$ in order
to keep the error down.  The same holds for $y$.

We can minimize the error by taking the partial derivative:
\begin{equation}
\frac{\partial E}{\partial c} = \frac{-2(x-c)}{\sigma^2_x} +
\frac{-2(y-c)}{\sigma^2_y}
\end{equation}
Setting this equation equal to zero and solving for $c$:
\begin{eqnarray}
\frac{-2(x-c)}{\sigma^2_x} + \frac{-2(y-c)}{\sigma^2_y} & = & 0 \\
\frac{x}{\sigma^2_x} - \frac{c}{\sigma^2_x}
+ \frac{y}{\sigma^2_y} - \frac{c}{\sigma^2_y} & = & 0 \\
c \left( \frac{1}{\sigma^2_x} + \frac{1}{\sigma^2_y} \right)
& = & \frac{x}{\sigma^2_x} + \frac{y}{\sigma^2_y} \\
c & = & \frac{ \frac{x}{\sigma^2_x} + \frac{y}{\sigma^2_y} }
{ \frac{1}{\sigma^2_x} + \frac{1}{\sigma^2_y} }
\end{eqnarray}
This equation again makes intuitive sense.
Suppose that the variance of $x$ is much smaller than the variance of $y$.
Then the combined estimate $c$ is equal to $x$.  The same is true in the
other direction.
Suppose that the variances of $x$ and $y$ were equal, say to $S$.  Then
the combined estimate $c$ is the mean (average) of $x$ and $y$.

Now we will manipulate the equation for $c$ algebraically.
If we scale the last equation for common denominators, we obtain:
\begin{eqnarray}
c & = & \frac{ \frac{\sigma^2_y x}{\sigma^2_x \sigma^2_y} +
\frac{\sigma^2_x y}{\sigma^2_x \sigma^2_y} }
{ \frac{\sigma^2_y}{\sigma^2_x \sigma^2_y} +
\frac{\sigma^2_x}{\sigma^2_x \sigma^2_y} }
\end{eqnarray}
Eliminating common denominators gives:
\begin{eqnarray}
c & = & \frac{\sigma^2_y x + \sigma^2_x y}{\sigma^2_y + \sigma^2_x}
\end{eqnarray}
Expanding in terms of $x$ and $y$ gives:
\begin{eqnarray}
c & = & \frac{\sigma^2_y}{\sigma^2_y + \sigma^2_x} x +
\frac{\sigma^2_x}{\sigma^2_y + \sigma^2_x} y
\end{eqnarray}
Now comes a little trick.  How far away is the $x$ term from a whole
$\frac{1}{1} x$?
Rewriting just that part gives:
\begin{eqnarray}
c & = & x - \frac{\sigma^2_x}{\sigma^2_y + \sigma^2_x} x +
\frac{\sigma^2_x}{\sigma^2_y + \sigma^2_x} y
\end{eqnarray}
Combining terms with common fractions gives:
\begin{eqnarray}
c & = & x + \frac{\sigma^2_x}{\sigma^2_y + \sigma^2_x} (y-x) \label{combine_eq}
\end{eqnarray}
Looking back at equation \ref{update_eq} we see that equation \ref{combine_eq}
has the same form, but the weight has been calculated as a function of
the variances.
In the Kalman filter, the two estimates of an unknown quantity come from
sensor readings and predictions from state transition equations.
They are combined in the update part of the filter according to
a function of their variances.



\end{document}
