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added fast fingerprinting method to related work

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toni
2018-10-19 17:18:27 +02:00
parent 565166e0b2
commit 837963b4e8
3 changed files with 543 additions and 6 deletions
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tex_review/chapters/relatedwork.tex
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@@ -11,7 +11,7 @@ Here, new particles are drawn according to some importance distribution, often r
%\todo{statt dynamics of the system vlt: the pedestrian's movement?}
Those particles are then weighted by the state evaluation given different sensor measurements.
A resampling step is deployed to prevent that only a small number of particles have a significant weight \cite{chen2003bayesian}.
Most localization approaches differ mainly in how the transition and evaluation steps are implemented and the sensors are incorporated \cite{Fetzer-16, Ebner-16, Hilsenbeck2014}.
Most localization approaches differ mainly in how the transition and evaluation steps are implemented and the sensors are incorporated \cite{Liao2003, Solin2016, jaworski2017real, Hilsenbeck2014}.
%\todo{hier ist irgendwie ein harter cut zu dem nächsten satz}
%Additionally, within this paper we present a method, which is designed to run solely on a commercial smartphone.
@@ -23,7 +23,7 @@ The system's dynamics describe a pedestrian's potential movement within the buil
This can be formulated as the question \emph{``Given the pedestrian's current position and heading are known, where could he be after a certain amount of time?''}.
Obviously, the answer to this question depends on the pedestrian's walking behavior, any nearby architecture and thus the building's floorplan.
%
Assuming the pedestrian to walk almost straight towards his current heading with a known, constant walking speed, the most basic form of state transition simply rejects all movements, where the line-of-sight between current position and potential destination is blocked by an obstacle \cite{Ebner-15}.
Assuming the pedestrian to walk almost straight towards his current heading with a known, constant walking speed, the most basic form of state transition simply rejects all movements, where the line-of-sight between current position and potential destination is blocked by an obstacle \cite{Woodman08-PLF, Blanchart09}.
%
Despite its simplicity, this approach suffers from several drawbacks.
The intersection-test can be costly, depending on the number of used particles and the complexity of the building.
@@ -59,8 +59,13 @@ Indoor localization using \docWIFI{} fingerprints was first addressed by \cite{r
During a one-time offline-phase, a multitude of reference measurements are conducted.
During the online-phase the pedestrian's location is then inferred by comparing those prior measurements against live readings.
Based on this pioneering work, many further improvements where made within this field of research \cite{PropagationModelling, ProbabilisticWlan, meng11}.
However, despite a very high accuracy up to \SI{1}{\meter}, fingerprinting approaches suffer from tremendous setup- and maintenance times.
Using robots instead of human workforce might thus be a viable choice, still this seems not to be a valid option for old buildings with limited accessibility due to uneven grounds and small stairs.
However, despite a very high accuracy up to \SI{1}{\meter}, classic fingerprinting approaches suffer from tremendous setup- and maintenance times.
\add{For this reason, some alternative approaches were presented to speed up the offline phase.
In \cite{Guimaraes16} the positions of recorded references are interpolated between the start and end of some reference path, based on the pedestrians gait cycle.
Unrecorded positions are then interpolated using the flood fill algorithm.
However, for old buildings with many nooks and crannies this might cause problems as the RSSI can differ highly within a few meter, especially in the entrance area of thick-walled rooms.
This could open the need for more advanced map interpolation methods or a higher number and density of reference paths to walk.
Another often considered alternative is using robots instead of human workforce \cite{he2016wi, yeh2009indoor}}, still this seems not to be a valid option for old buildings with limited accessibility due to uneven grounds and small stairs.
%wifi, signal strength
Signal strength prediction models are a well-established field of research to determine signal strengths for arbitrary locations by using an estimation model instead of real measurements.
@@ -79,8 +84,7 @@ They use a genetic optimization algorithm to estimate the parameters for a signa
The estimated parameters can be refined using additional walks.
Within this work we present a similar optimization approach for estimating the AP's location in 3D.
However, instead of taking multiple measuring walks, the locations are optimized based only on some reference measurements, further decreasing the setup-time.
Additionally, we will show that such an optimization scheme can partly compensate for the above abolished intersection-tests.
\commentByToni{Die Quelle aus den Reviews. Wir können auch Kontinuierlich. Der hat das Problem das er entweder überall gewesen sein muss, oder interpolieren.}
Additionally, we will show that such an optimization scheme can partly compensate for the above abolished intersection-tests.
%immpf
Besides well chosen probabilistic models, the system's performance is also highly affected by handling problems which are based on the nature of \add{a} particle filter.