Thesis: WuotD: beacon

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Eric Teunis de Boone 2023-04-18 16:54:28 +02:00
parent 6aa4e97e68
commit 2ecc48643c
2 changed files with 103 additions and 30 deletions

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@ -174,8 +174,9 @@ Taking one antenna as the reference antenna with $(\tClock)_r = 0$, the mismatch
% signals to send, and measure, (\tTrueArriv)_i.
In the former, the mechanism of measuring $(\tMeasArriv)_i$ from the signal has been deliberately left out.
The nature of the beacon allows for different methods to determine $(\tMeasArriv)_i$.\Todo{reword towards next sections?}
The nature of the beacon allows for different methods to determine $(\tMeasArriv)_i$.
In the following, two approaches for measuring $(\tMeasArriv)_i$ are examined.
\Todo{reword towards next sections?}
%%%%
@ -286,17 +287,22 @@ This relies on the ability of counting how many beacon periods have passed since
\bigskip
% Yay for the sine wave
In the following, the scenario of a (single) sine wave as a beacon is worked out.
This involves the tuning of the signal strength to attain the required accuracy.
Later, a mechanism to lift the period degeneracy using an airshower as discrete signal is shown.
In the following section, the scenario of a (single) sine wave as a beacon is worked out.
It involves the tuning of the signal strength to attain the required accuracy.
Later, a mechanism to lift the period degeneracy using an airshower as discrete signal is presented.
%%
%% Phase measurement
\subsection{Phase measurement}
A continuous beacon can syntonise antennas by correcting for the measured difference in beacon phase $(\Delta \pMeasArriv)_{ij}$.
The beacon phase can be derived from an antenna trace by applying a Fourier Transform to the data.
\\
The trace will contain noise from various sources external and internal to the detector such as
A continuous beacon can syntonise an array of antennas by correcting for the measured difference in beacon phases $(\Delta \pMeasArriv)_{ij}$.
They are derived by applying a \gls{FT} to the traces of each antenna.
The digital measurement of the beacon phase is dependent on at least two factors:
the strength of the beacon in comparison to other signals (such as noise) and the length of the traces.
Additionally, the \gls{FT} can be performed in a number of ways.
These aspects are examined in the following section.
\begin{figure}[h]
\begin{subfigure}{0.45\textwidth}
@ -330,35 +336,94 @@ The trace will contain noise from various sources external and internal to the d
}
\label{fig:beacon:ttl_sine_beacon}
\end{figure}
% DTFT
\subsubsection{Discrete Time Fourier Transform}
% FFT common knowledge ..
The typical \gls{FT} to obtain spectral information from periodic data is the \gls{FFT} (a fast implementation of the \gls{DFT} \eqref{eq:fourier:dft}).
Such an algorithm efficiently finds the magnitudes and phases within a trace $x$ at specific frequencies $f = f_s \tfrac{k}{N}$ determined solely by the number of samples $N$ ($0 \leq k < N$) and the sampling frequency $f_s$.
\\
% .. but we require a DTFT
Depending on the frequency of the beacon, the sampling frequency and the number of samples, one can resort to use such a \gls{DFT}.
However, if the frequency of interest is not covered in the specific frequencies, the approach must be modified (e.g. zero-padding or interpolation).
Especially when a single frequency is of interest, a shorter route can be taken by evaluating a discretized \gls{FT} directly.
\\
% DTFT from CTFT
Spectral information in data can be obtained using a \acrlong{FT}.
\begin{equation}
\label{eq:fourier}
X(\omega) = \frac{1}{2\pi} \int \dif{t}\, x(t)\, e^{i \omega t}
X(f) = \frac{1}{2\pi} \int_\infty^\infty \dif{t}\, x(t)\, e^{i 2 \pi f t}
\end{equation}
The general (continuous) \gls{FT} \eqref{eq:fourier} can be discretized in time to result in the \acrlong{DTFT}:
\begin{equation}
\label{eq:fourier:dtft}
X(\omega) = \frac{1}{2\pi N} \sum_{n=0}^N x(t[n])\, e^{i \omega t[n]}
X(f) = \frac{1}{2\pi N} \sum_{n=0}^{N-1} x(t[n])\, e^{i 2 \pi f t[n]}
\end{equation}
where $X(f)$ is the transform of $x(t)$ at frequency $f$, sampled at $t[n]$.
\\
\bigskip
% DFT sampling of DTFT / efficient multifrequency FFT
When the sampling of $x(t)$ is equally spaced, the $t[n]$ terms can be decomposed as a sequence, $t[n] = \tfrac{n}{f_s}$ such that \eqref{eq:fourier:dtft} becomes the \acrlong{DFT}:
\begin{equation}
\label{eq:fourier:dft}
X_k = \frac{1}{N} \sum_{n=0}^{N-1} x[n]\, \cdot e^{ \frac{i 2 \pi}{N} k n }
\phantom{.}
X(k) = \frac{1}{N} \sum_{n=0}^{N-1} x[n]\, \cdot e^{ i 2 \pi {\frac{k n}N} }
.
\end{equation}
with $\omega = \tfrac{k}{N}$.
% FT term to phase and magnitude
\bigskip
The magnitude of at frequency $f$
\bigskip
% Beacon frequency known -> single DTFT run
When the beacon frequency is known, a single \gls{DTFT} needs to be evaluated.
From this $X(f)$, the magnitude $A$ and phase $\pTrue$ are derived using
\begin{equation}
\label{eq:magnitude_and_phase}
\phantom.
A(f) = {\left|X(f)\right|}^2
\hfill
\pTrue(f) = \arctantwo\left(\Re(X(f)), \Im(X(f))\right)
.
\end{equation}
The decomposition of $X(f)$ into a real and imaginary part
With a constant beacon frequency, the coefficients for evaluating the \gls{DTFT} can be put into the hardware of the detectors.
% Beacon frequency unknown -> either zero-padding FFT or DTFT grid search
% Removing the beacon from the signal trace
% Signal to noise
\subsubsection{Signal to Noise}
% Gaussian noise
The traces will contain noise from various sources, both internal (e.g. LNA) and external (e.g. radio communications) to the detector.
Adding gaussian noise to the traces in simulation gives a simple noise model, associated to many random noise sources.
Especially important is that this simple noise model will affect the phase measurement depending on the strength of the beacon with respect to the noise level.
\\
\bigskip
Phasor concept
\cite{goodman1985:2.9}
Known phasor $\vec{s}$ + random phasor $\vec{m} = a e^{i\pTrue}$ with $-\pi < \pTrue < \pi$ and $a \geq 0$.
\begin{equation}
\label{eq:phasor_pdf}
\label{eq:random_phasor_pdf}
p_{A\PTrue}(a, \pTrue; s, \sigma)
= \frac{a}{2\pi\sigma^2}
\exp[ -
@ -373,20 +438,8 @@ Known phasor $\vec{s}$ + random phasor $\vec{m} = a e^{i\pTrue}$ with $-\pi < \p
requiring $ -\pi < 0 \leq pi $ and $a > 0$, otherwise $p_{A\PTrue} = 0$.
\bigskip
Rician distribution ( 2D Gaussian at $\nu$ with $\sigma$ spread)
\begin{equation}
\label{eq:amplitude_pdf:rice}
p^{\mathrm{RICE}}_A(a; s, \sigma)
= \frac{a}{\sigma^2}
\exp[-\frac{a^2 + s^2}{2\sigma^2}]
\;
I_0\left( \frac{a s}{\sigma^2} \right)
\end{equation}
with $I_0(z)$ the modified Bessel function of the first kind with order zero.
No signal $\mapsto$ Rayleigh ($s = 0$);
Large signal $\mapsto$ Gaussian ($s \gg a$)
\bigskip
Noise only Amplitude:
Rayleigh distribution
\begin{equation}
\label{eq:amplitude_pdf:rayleigh}
@ -404,10 +457,24 @@ Gaussian distribution
\end{equation}
\bigskip
Rician phase distribution: uniform (low $s$) + gaussian (high $s$)
Rician distribution ( 2D Gaussian at $\nu$ with $\sigma$ spread)
\begin{equation}
\label{eq:phase_pdf:full}
\label{eq:amplitude_pdf:rice}
p^{\mathrm{RICE}}_A(a; s, \sigma)
= \frac{a}{\sigma^2}
\exp[-\frac{a^2 + s^2}{2\sigma^2}]
\;
I_0\left( \frac{a s}{\sigma^2} \right)
\end{equation}
with $I_0(z)$ the modified Bessel function of the first kind with order zero.\\
No signal $\mapsto$ Rayleigh ($s = 0$);\\
Large signal $\mapsto$ Gaussian ($s \gg a$)
\bigskip
Random Phasor Sum phase distribution: uniform (low $s$) + gaussian (high $s$)
\begin{equation}
\label{eq:phase_pdf:random_phasor_sum}
p_\PTrue(\pTrue; s, \sigma) =
\frac{ e^{-\left(\frac{s^2}{2\sigma^2}\right)} }{ 2 \pi }
+

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@ -85,6 +85,7 @@
\newcommand{\Corr}{\operatorname{Corr}}
%\newcommand{\erf}{\operatorname{erf}}
\DeclareMathOperator{\arctantwo}{arctan2}
% Units
@ -111,3 +112,8 @@
\newacronym{PA}{PA}{Pierre~Auger}
\newacronym{PAObs}{PAO}{Pierre~Auger Observatory}
\newacronym{AERA}{AERA}{Auger Engineering Radio Array}
\newacronym{DTFT}{DTFT}{Discrete Time Fourier Transform}
\newacronym{DFT}{DFT}{Discrete Fourier Transform}
\newacronym{FFT}{FFT}{Fast Fourier Transform}
\newacronym{FT}{FT}{Fourier Transform}