Not much

tex/chapters/3_ftm.tex

@@ -37,12 +37,13 @@
 \section{Wi-Fi Range Measurements}
 \label{sec:ftm}
 
-An obvious approach to estimate a location is to measure the distance between the current unknown position and a known position.
+An obvious approach to estimate a location is to measure the distance between the current unknown position and known positions.
 Given multiple measurements to different reference points an absolute position in a local coordinate system can be found.
 With ideal distance measurements to several known positions it is straightforward to calculate the current position.
-However, in the present of noisy and imperfect measurements estimating a accurate position is a challenging problem.
+However, in the present of noisy and imperfect measurements estimating a precise position is a challenging problem.
+%TODO Harte bruch 
 For a smartphone based indoor localization system using the existing Wi-Fi infrastructure is a reasonable choice.
-In this work signal strength based and signal propagation time based distance measurements are considered.
+In this work signal strength and signal propagation time based distance measurements are considered.
 
 \subsection{Received Signal Strength Indication}
 % TODO dBm vs dB??
@@ -133,9 +134,10 @@ However, to account for the signal processing delay of the initiator's hardware
 When the ACK frame is received at the responder at time $t_4$ the responder can calculate the round trip time of the signal by subtracting $t_1$ from $t_4$.
 To exclude the processing delay of the initiator the difference between $t_2$ and $t_3$ is subtracted from the total round trip time, which results in the propagation delay of the signal
 \begin{equation}
-  \text{ToF} = (t_4-t_1) - (t_3-t_2) % TODO besseres Symbol als RTT
+  \text{ToF} = (t_4-t_1) - (t_3-t_2) \text{.} % TODO besseres Symbol als RTT
 \end{equation}
 
+Measuring ToF only once is usually not sufficient. 
 While RF power is relatively simple to measure, obtaining accurate ToF values at a small resolution like nanoseconds needs much more caution, as the measurements are sensitive to noise.
 Relatively small deviations from the real time value result in a vast error in the distance estimate, \eg a measurement error of \SI{10}{ns} results in a distance error of \SI{3}{m}.
 For this reason the above outlined procedure is repeated multiple times to reduce the impact of noise.
@@ -145,9 +147,13 @@ In fact, a single FTM measurement or burst instance, consists of many FTM-ACK ex
 \end{equation}
 
 After calculating the average ToF the responder transfers the result to the initiator where the result can be processed by an application.
-With increasing $n$ the impact of noise is lessened, but the time until the FTM measurement is available for the consuming software increases.
+With increasing $n$ the impact of noise is lessened, but the delay until the FTM measurement is available for the consuming software increases.
 Therefore, the actual choice of the value of $n$ is a trade-off between precision and measurement delay.
 
+Assuming that the signal propagates constantly at the speed of light the distance between initiator and responder is trivially given with
+\begin{equation}
+  d = \frac{\text{ToF}}{2} \cdot c
+\end{equation}
 %TODO ToF -> distance ToF/2 * c
 %TODO IEEE 802.11-2016 6.3.58.1
 
@@ -170,6 +176,6 @@ To allow much finer resolution the receiver uses super resolution methods to all
 In addition to distance measurements the \ieeWifiFTM standard defines a format to transfer location information about the responder.
 This allows to add new access points dynamically to the localization system without updating the initiators, \ie smartphone, as the access point can be configured to know its position and can transmit this information to the smartphone.
 
-Error sources:
+%Error sources:
-multipath propagation, noise, finite sample rate
+%multipath propagation, noise, finite sample rate
 

tex/chapters/4_ftmloc.tex

@@ -1,3 +1,8 @@
+% Notation:
+% Menge aller APs und deren Position
+% Gemessene Distanz vs true Distanz vs Schätzung
+% Selbe für Position
+
 \section{Position Estimation}
 \label{sec:position}
 
@@ -38,7 +43,7 @@ This offset depends on the particular choice of equation to subtract.
 
 Another factor which influences the localization accuracy is the geometry of the setup where the measurements were taken.
 The accuracy of multilateration estimate depends on the position of the APs and the position of the smartphone relatively to each other.
-Therefore, it is important to consider the actual AP locations for improved localization accuracy and the walkable area where the localization system is used.
+Therefore, it is important to consider the actual AP locations and the walkable area where the localization system is used to improve localization accuracy.
 However, the best geometrical setup for localization is not necessarily the best setup for signal coverage.
 
 Best localization results are archived when the distance circles of the APs intersect in a near orthogonal angle.
@@ -46,34 +51,49 @@ Localization performance degrades with wider intersection angles.
 The optimal setup in a simple scenario given four APs is to place them at the corners of a square.
 This yields best localization performance inside of the square.
 
-Usually non-optimal AP locations need to be chosen due to environmental constrains like building structure and signal coverage.
+Usually non-optimal AP locations need to be chosen due to environmental constrains like building structure or signal coverage.
-These geometrical considerations can be founded on geometric dilution of precision (GDOP), which is a indicator which specifies the localization error based on the sender-receiver geometry.
+These geometrical considerations can be founded on geometric dilution of precision (GDOP), which is a rating of the expected localization performance based on the sender-receiver geometry.
-%DOP ganz nett aber signalstärken spielt auch eine Rolle
+Lower GDOP values indicate better estimation precision due to smaller intersection angles.
+GDOP allows to plan the deployment of APs for location estimation before actually installing the APs.
 
+Given a setup with $i$ APs and their known positions $\mPosVec_i = (x,y)^T$, the GDOP value is calculated by forming a matrix 
+%In order to compute the GDOP value in a setup with known AP positions $\mPosVec_i = (x,y)^T$ their correspondent unit vectors from a given position $(x,y)$ form the matrix
 \begin{equation}
 A = 
  \begin{pmatrix}
-  \frac{(x_1 - x)}{R_1} & \frac{(y_1 - y)}{R_1} & \frac{(z_1 - z)}{R_1} \\[0.3em]
+  \frac{(x_1 - x)}{R_1} & \frac{(y_1 - y)}{R_1}  \\[0.3em]
-  \frac{(x_2 - x)}{R_2} & \frac{(y_2 - y)}{R_2} & \frac{(z_2 - z)}{R_2} \\[0.3em]
+  \frac{(x_2 - x)}{R_2} & \frac{(y_2 - y)}{R_2}  \\[0.3em]
-  \vdots                & \vdots                & \vdots                \\[0.3em]
+  \vdots                & \vdots                 \\[0.3em]
-  \frac{(x_i - x)}{R_i} & \frac{(y_i - y)}{R_i} & \frac{(z_i - z)}{R_i} \\
+  \frac{(x_i - x)}{R_i} & \frac{(y_i - y)}{R_i}  \\
  \end{pmatrix}
 \end{equation}
-where $R_i=\sqrt{(x_i-x)^2+(y_i-y)^2+(z_i-z)^2}$
+where $R_i=\sqrt{(x_i-x)^2+(y_i-y)^2}$. Each row of $A$ is a unit vector from a given position $(x,y)^T$ to the AP's position $\mPosVec_i$. Given the matrix $Q = (A^TA)^{-1}$ the GDOP value is the square root of the sum of the diagonal of $Q$:
-
-\begin{equation}
-  Q = (A^TA)^{-1}
-\end{equation}
-
 \begin{equation}
 \text{GDOP} = \sqrt{\text{trace}(Q)}
 \end{equation}
 
+While GDOP allows to rate the given scenario regarding position estimation precision it only takes the relative geometry of the senders and receiver into account.
+Other factors like signal attenuation or absorption are not considered.
+However, GDOP still gives a good first impression of the theoretical suitability of a geometrically arrangement of senders for position estimation and provides an indicator for further optimizations of it.
+%DOP ganz nett aber signalstärken spielt auch eine Rolle
+
 \subsection{Probabilistic}
 %Dichte aus Messungen erzeugen.
 %Distanzern werden mit Normalverteilung gewichtet
 %Vorteil: Nicht ideale Schnittpunkte sind kein Problem, weil die Dichte sowas abbilden kann
 %FTM Vorteil: Fehlen von Messungen kann probabilistisch erfasst werden indem Streuung der NV größer wird / Oder es enstehen einfach mehrere Hypthesen über die Position
+Probabilistic frameworks allow to natively incorporate faulty or inaccurate measurements into the position model.
+While multilateration produces a single point as position estimate the probabilistic framework allows to describe the estimated position as probability density function.
+This approach has the advantage to model inaccurate measurments as variances and quantify them.
+
+Assuming that FTM measurements follow a Gaussian distribution, the measured distance $d_i$ between the sender and AP $i$ is described by $\mathcal{N}(d^*_i, \sigma^2_i)$ where $d^*_i=\|\mPosVec_i - \mPosVec \|$ is the actual distance between the AP position $\mPosVec_i$ and a given position $\mPosVec$. $\sigma^2_i$ is the variance of the measurement.
+Visualized on a two dimensional plane this produces a ring shaped density function where the cross section of the ring is the well known Gaussian curve.
+
+Multiple distance measurements can be combined by a joint density function, if the individual distance measurements to the APs are statistical independent.
+The probability to observe a distance measurement $d$ at position $\mPosVec$ is given as
+\begin{equation}
+  p(d | \mPosVec ) = \prod_i \frac{1}{\sqrt{2\pi\sigma^2_i}} \exp \left( - \frac{(d - d^*_i)^2}{2\sigma^2_i} \right)
+\end{equation}
 
 \subsection{Particle Filter}
 %Warum auch noch PF? Weil... die meisten lokalisierungs systeme diesen als nicht-linearen schätzer benutzen. er ist vielseitig und kann einfach mit anderen sensoren kombiniert werden. ist das gängigste sensor fusionsverfahren. die dichte wird in samples repräsentiert und ist damit nur eine approximatino der wahrsch. dichte.. dadurch ganz ander repräsentation als probabilistic teil. außerdem ist es ein FILTER, hat also vergangenheit mit drin.