%First-principle analysis of GPS dynamics: \emph{time to first
% fix}. Comparison with empirical analysis from the state of the art
%(check that numbers match the python-nokia implementation or whatever
%else is available). Implementation issues with existing solutions
%(there are some unjustified delays -- probably introduced by the
%software and software bugs -- that could be eliminated).
%
%Additionally, decribe \emph{phenomena} like loss of ephemeris and
%randing data and what are the delays introduced because of that. Say
%that losing the ephemeris data means basically having the GPS receiver
%turned off for ``too long'' and losing the ranging data is mostly
%equivalent to a worst case in losing visibility of the satellites. If
%you want to distinguish, you can have a finite state machine for each
%satellite.
In this section we will discuss two implementations of the model specified above: one in the modeling language Modelica\footnote{www.modelica.org} and a second one in the Matlab language. The purpose of the first one is to show how the model captures the relevant dynamics of a GPS sensor and the object-oriented nature of the Modelica language makes it ready to use for other applications. The second implementation is instead used to present how the model can be combined with the sensor fusion algorithm discussed in section~\ref{sec:fusion} to evaluate the possible accuracy-over-battery-consumption trade-offs. The two implementations also correspond to the two parts in which this section is organized.
\subsection{GPS sensor dynamics}
\label{sec:res:gps}
%simulations: time to first fix, loss of ephemeris data, loss of visibility
The phenomena we will show in this section are: the TTFF, the loss of ephemeris data and the loss of visibility.
Figure~\ref{fig:control1} shows the command signal used to show the time that passes at the start up beofore the position bcomes available. As we can see, first the sensor is kept turned on for one minute and then it is sampled at regular intervals. The resulting position availability of the sensor is then shown in figure~\ref{fig:position1}.
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@@ -31,20 +45,7 @@ Being aware of those differences -- the extra software layers included in the ex
------------------------------------
First-principle analysis of GPS dynamics: \emph{time to first
fix}. Comparison with empirical analysis from the state of the art
(check that numbers match the python-nokia implementation or whatever
else is available). Implementation issues with existing solutions
(there are some unjustified delays -- probably introduced by the
software and software bugs -- that could be eliminated).
Additionally, decribe \emph{phenomena} like loss of ephemeris and
randing data and what are the delays introduced because of that. Say
that losing the ephemeris data means basically having the GPS receiver
turned off for ``too long'' and losing the ranging data is mostly
equivalent to a worst case in losing visibility of the satellites. If
you want to distinguish, you can have a finite state machine for each
