fixed comments

tex/chapters/abstract.tex

@@ -1,5 +1,5 @@
 \begin{abstract}
-Indoor localisation continuous to be a topic of growing importance. \commentByLukas{Wuerde "Many different.." Satz weglassen, weil informationslos} Many different approaches for estimating the position of a pedestrian are presented year after year. 
+Indoor localisation continuous to be a topic of growing importance. 
 Despite the advances made, several profound problems are still present. 
 For example, estimating an accurate position from a multimodal distribution or recovering from the influence of faulty measurements.
 Within this work, we try to solve such problems with help of Monte Carlo smoothing methods, namely forward-backward smoother and backward simulation.

tex/chapters/experiments.tex

@@ -1,16 +1,20 @@
 \section{Experiments}
 
-ddd \cite{Ville09} dddd
+%Es gibt zwei Arten von fehlern. Mit zeitlicher und ohne zeitliche Information. 
+
+
+
+%Smoothing mit großen lag kann die zeitliche information schwer halten. das liegt hauptsächlich daran, das im smoothing nur die relativen positionsinfos genutzt werden. das wi-fi wird nicht beachtet und deswegen können absolute justierungen der position (sprünge) nur sehr schlecht abgefedert werden. 
+
 
 Evaluation:
 \begin{itemize}
 	\item Filter ist immer der gleiche mit MultiPathPrediction und Importance Factors
-	\item FBS Interval mit 500 und 7500 Partikeln auf 4 Pfaden mit SimpleSmoothingTrans
-	\item BS Interval mit 500 zu 50 und 7500 zu 500 Partikeln auf auf 4 Pfaden mit SimpleSmoothingTrans
-	\item FBS Lag = 5 mit 500 und 7500 Partikeln auf 4 Pfaden mit SimpleSmoothingTrans
-	\item BS Lag = 5 mit 500 zu 50 und 7500 zu 500 Partikeln auf auf 4 Pfaden mit SimpleSmoothingTrans
+	\item FBS Interval mit 500 und 2500 Partikeln auf 4 Pfaden mit SimpleSmoothingTrans
+	\item BS Interval mit 500 zu 100 und 2500 zu 500 Partikeln auf auf 4 Pfaden mit SimpleSmoothingTrans
+	\item FBS Lag = 5 mit 500 und 2500 Partikeln auf 4 Pfaden mit SimpleSmoothingTrans
+	\item BS Lag = 5 mit 500 zu 100 und 2500 zu 500 Partikeln auf auf 4 Pfaden mit SimpleSmoothingTrans
 	\item BS Lag zu Error Plot. Lag von 0 bis 100, wie verhält sich der Error. Am besten auf Pfad 4 mit SimpleSmoothingTrans.
-	\item BS Lag = 5 mit 500 Partikeln auf einem Pfad der manuell angepasst ist (mach ich) mit DijkstraTrans.
 \end{itemize}
 
 

tex/chapters/introduction.tex

@@ -4,7 +4,7 @@
 %Obviously, GNSS are of no practical use in the context of indoor localisation.  
 
 Determining a position indoors is a challenging task. 
-Besides the complex architecture of many buildings, \commentByLukas{kein a?} a high accuracy needs to be achieved, especially for buildings with many small separated areas like shopping malls or office blocks.
+Besides the complex architecture of many buildings, high accuracy needs to be achieved, especially for buildings with many small separated areas like shopping malls or office blocks.
 In recent years, many different systems were presented to meet those requirements. 
 Especially Wi-Fi positioning and pedestrian dead reckoning (PDR) are very popular solutions. 
 Approaches based on PDR try to estimate the current position given the previous position and thus require an initial state. 
@@ -14,21 +14,18 @@ Additional improvements can be achieved by using environmental information about
 
 In most cases, probabilistic methods are used to incorporate those highly different sensor types. 
 Here, a probability distribution describes the pedestrian's possible whereabouts and therefore the uncertainty of the system. 
-Drawing \commentByLukas{Drawing samples oder sampling} from a probability distribution and
-\commentByLukas{Willst du hier das Samplen erwaehnen? Es kommt so ein bisschen aus dem Nichts und man kann es gerade nur schwer einordnen} finding an analytical solution for densities is in most cases a difficult task, especially in case of time sequential, non-linear and non-Gaussian models. 
+Drawing 
+\commentByLukas{Drawing samples oder sampling} 
+from a probability distribution and
+\commentByLukas{Willst du hier das Samplen erwaehnen? Es kommt so ein bisschen aus dem Nichts und man kann es gerade nur schwer einordnen} 
+\commentByToni{Ich möchte hier auf Monte Carlo ueberleiten. Warum macht man das ueberhaupt? Ich finde das umschreibt das ganz gut. alles andere kostet nur unfassbar viel platz wie ich finde.}
+finding an analytical solution for densities is in most cases a difficult task, especially in case of time sequential, non-linear and non-Gaussian models. 
 Due to the high complexity of the human movement, we consider indoor localisation as such.
 
-A broad class to obtain numerical results instead are \commentByLukas{kein the} the Monte Carlo (MC) methods. 
-Here, a set of weighted random samples is used to solve any problem having a probabilistic interpretation. \commentByLukas{.. is used to solve the estimation process? Man loest ja nicht jedes probabilistische Problem damit?}
-By applying the time sequential hidden Markov process of Bayes filtering, one of the most important MC techniques results: particle filtering. 
-A particle filter updates the state estimation recursively in time with every new incoming measurement using the state transition and state evaluation step. \commentByLukas{Das macht ja ganz allgemein der Bayes Filter und ist nicht nur spezifisch fuer Partikel filter}
-
-\commentByLukas{Vielleicht ein wenig umschreiben?:
 Bayesian filters solve such problems by updating the state estimation recursively with every new incoming measurement. 
 A broad class to obtain numerical results for this approach are Monte Carlo (MC) methods, where a set of random samples is used to approximate the underlying probability distribution. 
-
 By applying the time sequential hidden Markov process of Bayes filtering, one of the most important MC techniques results: particle filtering. 
-Here, a set of weighted random samples is used to solve the state estimation process.}
+Here, a set of weighted random samples is used to solve the state estimation process.
 
 Based on this general methodology, many different approaches for estimating a position in indoor environments have been developed. 
 All these approaches differ mainly in how the dynamics are modelled in the transition step and how a specific sensor measurement can be used for evaluation. 
@@ -37,8 +34,7 @@ The evaluation model is mostly separated into any number of sensor models, each
 For example, a barometer can be used to determine the probability of being on a certain floor \cite{Binghao13-UBI}. 
 %Another example that demonstrates the big differences between single approaches is the large number of sensor models using Wi-Fi signal strengths. There are fingerprinting methods, which require an extensive offline calibration phase, signal strength prediction models like the log-distance model or wall-attenuation-factor model and many others \cite{Ville09, Fang09, Ebner:Thesis:2013}.
 
-Despite the many advances made in the last years, nearly all systems suffer from more or less the same problems \commentByLukas{Wuerde jetzt eine Aufzaehlung der Probleme der einzelnen Systeme erwarten. Stattdessen geht es direkt weiter mit Problemen von Sensormodellen. Den naechsten Satz vielleicht einfach weglassen, dann sollte es ok sein.}. 
-Of course, every sensor model brings its very own weaknesses. 
+Despite the many advances made in the last years, nearly all systems suffer from more or less the same problems.
 Like mentioned before, PDR suffers from an accumulating bias,
 the signal of Wi-Fi gets attenuated by walls
 \commentByFrank{falls noch platz ist: noch mehr nachteile :P \docWIFI{} location estimation strongly depends on the quality of the signal-strength estimation model (oder fingerprinting) and the way the smartphone is held}
@@ -49,11 +45,10 @@ That is the reason for the use of statistical methods in the first place. Nevert
 
 Current transition models, which aim to approximate the movement, are still very restrictive and unable to handle unforeseen events. 
 Faulty sensor measurements, like a falsely detected turn, can cause the estimation to lose track.
-For example by taking \commentByFrank{by taking -> by recognising?} a turn too soon and walking into a room instead of another big hallway. 
+For example by recognising a turn too soon and walking into a room instead of another big hallway. 
 Due to this, the filter needs some time to recover, which again takes a while because of the restrictive model (e.g. no walking through walls and only realistic walking speed). 
 This temporal delay worsens the estimate immensely. 
-A solution to recover from such filter divergences faster, is using 
-\commentByFrank{is using -> involves?} methods to re-initialize the filtering procedure \cite{Nurminen2014}. 
+A solution to recover from such filter divergences faster, involves methods to re-initialize the filtering procedure \cite{Nurminen2014}. 
 However, even this can not completely prevent delays. 
 Another reason for possible time delays are slow sensor updates. 
 For example, most mobile devices restrict the Wi-Fi module to update only every few seconds, to save on battery. 
@@ -64,9 +59,7 @@ For example, most mobile devices restrict the Wi-Fi module to update only every
   \input{gfx/multimodalpath.eps_tex}
 	\caption[An example of the occurrence of a multimodal distribution.]{
 	An example of the occurrence of a multimodal distribution.
-	At time $t-1$ the floor is separated by a wall and the mode of the distribution (coloured circle),
-	\commentByFrank{mode of the weglassen? einfach: distribution ... splits}
-	 representing the current position, splits apart.
+	At time $t-1$ the floor is separated by a wall and the distribution (coloured circle), splits apart.
 	The most likely position (green line) is estimated somewhere in-between. After a right turn, the distribution slowly starts to recover its unimodality.}
 	\label{fig:multimodalPath}
 \end{figure}
@@ -90,13 +83,16 @@ Namely, forward-backward smoothing \cite{doucet2000} and backward simulation \ci
 
 Within this work, we investigate the benefits and drawbacks of those techniques using our indoor localisation system presented in \cite{Ebner-16}. 
 We provide both, fixed-lag and fixed-interval smoothing as well as a novel approach for incorporating them easily within the localisation procedure. 
+Additionally, we enrich the state transition model with an activity recognition to distinguish between walking, standing and walking stairs.
 The main goal is to solve above mentioned problems and to investigate new possibilities for even more advanced systems.
+All of our contributions are supported by an extensive experimental evaluation.
 
 
-
-\commentByLukas{In der Einleitung sollte an einer Stelle ganz klar die Contributions der Arbeit herausgestellt werden. Vielleicht irgendwie sowas: }
+\commentByLukas{In der Einleitung sollte an einer Stelle ganz klar die Contributions der Arbeit herausgestellt werden. Vielleicht irgendwie sowas: 
 The contributions of this work are as follows:
-Firstly, we extend current smoothing methods for indoor localisation to resolve multimodalities during the state estimation process. Secondly, we incorporate the knowledge of the user's destination as a-priori knowledge. Thirdly, we enrich the state transition model with an activity recognition to distinguish between walking, standing and walking stairs. All of our contributions are supported by an extensive experimental evaluation. 
+Firstly, we extend current smoothing methods for indoor localisation to resolve multimodalities during the state estimation process. Secondly, we incorporate the knowledge of the user's destination as a-priori knowledge. Thirdly, we enrich the state transition model with an activity recognition to distinguish between walking, standing and walking stairs. All of our contributions are supported by an extensive experimental evaluation. }
+
+\commentByToni{Steht doch direkt einen Absatz drueber nur halt kein plakatives "The contributions of " steht. Nur das Smoothing ist die Contribution meiner Meinung nach. Das prior knowledge ist ausm fusion paper. lediglich die activity rec fehlte. habe es ergänzt :). da bin ich mir aber noch nicht sicher... es wird ja nicht wirklich evaluiert sondern eher als "gegeben" angesehen. vielleicht dann auch eher so beschreiben?}
 
 
 

tex/chapters/relatedwork.tex

@@ -32,7 +32,7 @@ In recent years, smoothing gets attention mainly in other areas as indoor locali
 The early work of \cite{isard1998smoothing} demonstrates the possibilities of smoothing for visual tracking.
 They used a combination of the CONDENSATION particle filter with a forward-backward smoother. 
 Based on this pioneering approach, many different solutions for visual and multi-target tracking have been developed \cite{Perez2004}.
-For example, in \cite{Platzer:2008} a particle smoother is used to reduce multimodalities in a blood flow simulation for human vessels. \commentByLukas{Or nicht schoen am Satzanfang. In [] a smoother is used to... oder the authors of [] use a ...}Or \cite{Hu2014} uses a smoother to overcoming the problem of particle impoverishment while predicting the Remaining Useful Life (RUL) of equipment (e.g. a Lithium-ion battery).
+For example, in \cite{Platzer:2008} a particle smoother is used to reduce multimodalities in a blood flow simulation for human vessels. The authors of \cite{Hu2014} use a smoother to overcoming the problem of particle impoverishment while predicting the Remaining Useful Life (RUL) of equipment (e.g. a Lithium-ion battery).
 
 %smoothing im bezug auf indoor
 Nevertheless, their are some promising approaches for indoor localisation systems as well. 
@@ -45,17 +45,17 @@ However, a fixed-lag smoother was treated only in theory.
 In the work of \cite{Paul2009} both fixed-interval and fixed-lag smoothing were presented. 
 They implemented Wi-Fi, binary infra-red motion sensors, binary foot-switches and a potential field for floor plan restrictions. 
 Those sensors were incorporated using a sigma-point Kalman filter in combination with a forward-backward smoother.
-It was also proven by \cite{Paul2009}, that the fixed-lag smoother is slightly less accurate then the fixed-interval smoother. \commentByLukas{, as one would expect ...}
-As one would expect from the theoretical foundation.
+It was also proven by \cite{Paul2009}, that the fixed-lag smoother is slightly less accurate then the fixed-interval smoother, as one would expect from the theoretical foundation.
 Unfortunately, even a sigma-point Kalman filters is after all just a linearisation and therefore not as flexible and suited for the complex problem of indoor localisation as a non-linear estimator like a particle filter. 
 \commentByToni{Kann das jemand nochmal verifizieren? Das mit dem Kalman Filter. Danke.}
 \commentByLukas{Ich wuerde den Satz ganz weglassen. Ansonsten musst du angeben, wo die eigentlichen Probleme liegen, also z.B. in welchen konkreten Situation das Kalman Filter nicht mehr funktioniert usw. So ist es jetzt erstmal nur eine Behauptung ohne jeglichen Hintergrund.}
+\commentByToni{Ich bin mir nicht sicher ob das eine Behauptung ohne jeglichen Hintergrund ist. Meiner Meinung nach ist das ziemlich weitreichend bekannt. Finde den Satz persoenlich ganz gut, weil er uns deutlich von dieser Arbeit abgrenzt und das ist wichtig.}
 Additionally, the Wi-Fi RSSI model requires known calibration points and is deployed using a remarkable number of access points for very small spaces.  
-In our opinion this is not practical and we would further recommend adding a PDR-based transition instead of a random one. 
-\commentByLukas{Der erste Teil vom Satz bezieht sich auf die vielen Messungen? Das find ich ok. Der zweite Teil vom Satz hat dann damit aber gar nichts mehr zu tun? Auch hier muesste man begruenden warum eine zufaellige Transition schlecht ist.}
+In our opinion this is not practical and does not suite real-world conditions.
+Since humans with a specific destination in mind do not tend to change their directions randomly, we would further recommend adding a PDR-based transition to draw samples in a more directed manner instead of scattering them randomly in every direction.
 
-In contrast \commentByLukas{In contrast zu was? Wuerde ich weglassen. The here presented ...}, the here presented approach is able to use two different smoothing algorithm, both implemented as fixed-interval and fixed-lag versions. 
-Further, our localisation system presented in \cite{Ebner-16} enables us to walk stairs and therefore going into the third dimension.
+The here presented approach is able to use two different smoothing algorithm, both implemented as fixed-interval and fixed-lag versions. 
+Further, our localisation system presented in \cite{Ebner-16} enables us to walk stairs and thus going into the third dimension.
 Therefore, a regularly tessellated graph is utilized to avoid walls, detecting doors and recognizing stairs. 
 Within this work, this is additionally supported by a simple classification that detects the activities  unknown, standing, walking and walking stairs. 
 Finally, we incorporate prior navigation knowledge by using syntactically calculated realistic human walking paths \cite{Ebner-16}.