toni/IPIN2016
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Linguistic checking

Browse Source
This commit is contained in:
Lukas Koeping
2016-05-12 10:40:24 +02:00
parent ff56649a5b
commit b936668818
7 changed files with 44 additions and 45 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
tex/chapters/introduction.tex
View File

@@ -30,27 +30,27 @@ Here, a set of weighted random samples is used to solve the state estimation pro
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.
For example, recent approaches are using a graph-based structure to consider environmental restrictions (walking through walls) and the characteristics of human movement (walking speed) within the transition model \cite{Ebner-15, Nurminen2014, Hilsenbeck2014}.
For example, recent approaches are using a graph-based structure to consider environmental restrictions (walking through walls) and the characteristics of human movements (walking speed) within the transition model \cite{Ebner-15, Nurminen2014, Hilsenbeck2014}.
The evaluation model is mostly separated into any number of sensor models, each representing the probability for a noisy measurement in regard to the current position.
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.
Like mentioned before, PDR suffers from an accumulating bias,
the signal of \docWIFI{} gets attenuated by walls
As mentioned before, PDR suffers from an accumulating bias,
the signal of \docWIFI{} becomes 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}
and the barometric pressure is highly affected by weather patterns and humidity
%\commentByFrank{spontane fenster/tuer oeffnung}
\cite{Binghao13-UBI}.
That is the reason for the use of statistical methods in the first place. Nevertheless, there are even more profound problems regarding the whole position estimation procedure.
This is the reason for the use of statistical methods in the first place. Nevertheless, there are even more profound problems regarding the whole position estimation procedure.
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 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.
This temporal delay worsens the estimation immensely.
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.
However, even this cannot completely prevent delays.
Another reason for possible time delays are slow sensor updates.
For example, most mobile devices restrict the \docWIFI{} module to update only every few seconds, to save on battery.
%
@@ -67,7 +67,7 @@ For example, most mobile devices restrict the \docWIFI{} module to update only e
%
Further critical problems arise from multimodal distributions.
Those are caused by multiple possible position estimates.
Fig. \ref{fig:multimodalPath} illustrates an example where a floor gets separated by a wall.
Fig. \ref{fig:multimodalPath} illustrates an example where a floor is separated by a wall.
Due to inaccurate measurements and a PDR approach for evaluating the movement, the distribution splits apart.
Depending on the chosen estimator, the approximated position might be located somewhere in-between as seen in fig. \ref{fig:multimodalPath}.
Only after the pedestrian turns right, the distribution is again unimodal, since moving through walls is impossible.
@@ -80,14 +80,14 @@ However, standard filtering methods are not able to use any future information a
One promising way to deal with these problems is smoothing.
Smoothing methods are able to make use of future measurements for computing their estimation.
By running backwards in time, they are also able to remove multimodalities and improve the overall localisation result.
Since the problem of navigation, especially the representation of complex movement patterns, results in a non-linear and non-Gaussian state space, this work focuses mainly on smoothing techniques based on the broad class of MC methods.
Since the problem of navigation, especially the representation of complex movement patterns, results in a non-linear and non-Gaussian state space, this work focuses mainly on smoothing techniques based on the broad class of MC methods, namely
%Of course, this excludes linear procedures like Kalman filtering.
Namely, forward-backward smoothing \cite{doucet2000} and backward simulation \cite{Godsill04:MCS}.
forward-backward smoothing \cite{doucet2000} and backward simulation \cite{Godsill04:MCS}.
Within this work, we investigate the benefits and drawbacks of those techniques using a conventional localisation system \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.
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 the above mentioned problems and to investigate new possibilities for even more advanced systems.
The main goal is to solve the above-mentioned problems and to investigate new possibilities for even more advanced systems.
All of our contributions are supported by an extensive experimental evaluation.