From 9bbd9c7510f86e60a2b907ed1a4caeeeb1ebc4fb Mon Sep 17 00:00:00 2001 From: Lukas Koeping <=> Date: Mon, 2 May 2016 10:16:18 +0200 Subject: [PATCH] comments to abstract and introduction --- tex/chapters/abstract.tex | 4 ++-- tex/chapters/introduction.tex | 26 ++++++++++++++++++++------ 2 files changed, 22 insertions(+), 8 deletions(-) diff --git a/tex/chapters/abstract.tex b/tex/chapters/abstract.tex index 0b260df..9e08d80 100644 --- a/tex/chapters/abstract.tex +++ b/tex/chapters/abstract.tex @@ -1,8 +1,8 @@ \begin{abstract} -Indoor localisation continuous to be a topic of growing importance. 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. \commentByLukas{Wuerde "Many different.." Satz weglassen, weil informationslos} Many different approaches for estimating the position of a pedestrian are presented year after year. 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 do solve such problems with help of Monte Carlo smoothing methods, namely forward-backward smoother and backward simulation. +Within this work, we try to solve such problems with help of Monte Carlo smoothing methods, namely forward-backward smoother and backward simulation. In contrast to normal filtering procedures like particle filtering, smoothing methods are able to incorporate future measurements instead of just using current and past data. This enables many possibilities for further improving the position estimation. Both smoothing techniques are deployed as fixed-lag and fixed-interval smoother and a novel approach for incorporating them easily within our localisation system is presented. diff --git a/tex/chapters/introduction.tex b/tex/chapters/introduction.tex index b9e3f3e..9d6e19f 100644 --- a/tex/chapters/introduction.tex +++ b/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, 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, \commentByLukas{kein a?} a 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. @@ -14,12 +14,21 @@ 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 from a probability distribution and 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} 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 the Monte Carlo (MC) methods. -Here, a set of weighted random samples is used to solve any problem having a probabilistic interpretation. + +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. +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.} 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. @@ -28,7 +37,7 @@ 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. +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. Like mentioned before, PDR suffers from an accumulating bias, the signal of Wi-Fi gets attenuated by walls @@ -85,6 +94,11 @@ The main goal is to solve above mentioned problems and to investigate new possib +\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. + +