added fast fingerprinting method to related work
This commit is contained in:
@@ -92,6 +92,8 @@ This might already provide enough accuracy for some use-cases and buildings, but
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Therefore, instead of using a pure empiric model, we deploy an optimization scheme to find a well-suited set of parameters ($\mPosAPVec{}, \mTXP{}, \mPLE{}, \mWAF{}$) per \docAPshort{}, where $\mPosAPVec{} = (x,y,z)^T$ denotes the \docAPshort{}'s estimated position.
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The optimization is based on a set of reference measurements $\vec{s_{\text{opt}}}$ throughout the building, e.g. every \SI{3}{} to \SI{7}{\meter} centred within a corridor and between \SI{1}{} and \SI{4}{} references per room, depending on the room's size.
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Compared to classical fingerprinting, where reference measurements are recorded on small grids between \SI{1}{} to \SI{2}{\meter}, this highly reduces their required number and thus the overall setup-time.
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\add{Of course, their are fast fingerprinting solutions like \cite{Guimaraes16} (cf. section \ref{sec:relatedWork}), which are able to record the reference measurements while walking on predefined paths.
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Nevertheless, such an approach would also be compatible with the Wi-Fi model presented here and is thus a valid topic for future work.}
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The target function to optimize the $6$ model parameters for one \docAPshort{} is given by
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@@ -11,7 +11,7 @@ Here, new particles are drawn according to some importance distribution, often r
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%\todo{statt dynamics of the system vlt: the pedestrian's movement?}
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Those particles are then weighted by the state evaluation given different sensor measurements.
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A resampling step is deployed to prevent that only a small number of particles have a significant weight \cite{chen2003bayesian}.
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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}.
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Most localization approaches differ mainly in how the transition and evaluation steps are implemented and the sensors are incorporated \cite{Liao2003, Solin2016, jaworski2017real, Hilsenbeck2014}.
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%\todo{hier ist irgendwie ein harter cut zu dem nächsten satz}
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%Additionally, within this paper we present a method, which is designed to run solely on a commercial smartphone.
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@@ -23,7 +23,7 @@ The system's dynamics describe a pedestrian's potential movement within the buil
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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?''}.
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Obviously, the answer to this question depends on the pedestrian's walking behavior, any nearby architecture and thus the building's floorplan.
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%
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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}.
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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}.
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%
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Despite its simplicity, this approach suffers from several drawbacks.
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The intersection-test can be costly, depending on the number of used particles and the complexity of the building.
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@@ -59,8 +59,13 @@ Indoor localization using \docWIFI{} fingerprints was first addressed by \cite{r
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During a one-time offline-phase, a multitude of reference measurements are conducted.
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During the online-phase the pedestrian's location is then inferred by comparing those prior measurements against live readings.
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Based on this pioneering work, many further improvements where made within this field of research \cite{PropagationModelling, ProbabilisticWlan, meng11}.
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However, despite a very high accuracy up to \SI{1}{\meter}, fingerprinting approaches suffer from tremendous setup- and maintenance times.
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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.
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However, despite a very high accuracy up to \SI{1}{\meter}, classic fingerprinting approaches suffer from tremendous setup- and maintenance times.
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\add{For this reason, some alternative approaches were presented to speed up the offline phase.
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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.
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Unrecorded positions are then interpolated using the flood fill algorithm.
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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.
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This could open the need for more advanced map interpolation methods or a higher number and density of reference paths to walk.
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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.
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%wifi, signal strength
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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.
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@@ -80,7 +85,6 @@ The estimated parameters can be refined using additional walks.
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Within this work we present a similar optimization approach for estimating the AP's location in 3D.
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However, instead of taking multiple measuring walks, the locations are optimized based only on some reference measurements, further decreasing the setup-time.
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Additionally, we will show that such an optimization scheme can partly compensate for the above abolished intersection-tests.
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\commentByToni{Die Quelle aus den Reviews. Wir können auch Kontinuierlich. Der hat das Problem das er entweder überall gewesen sein muss, oder interpolieren.}
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%immpf
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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.
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booktitle={Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference on},
|
||||
pages={1--6},
|
||||
year={2011},
|
||||
organization={IEEE}
|
||||
}
|
||||
|
||||
@article{mehta2017vnect,
|
||||
title={{Vnect: Real-time 3D Human Pose Estimation with a Single RGB Camera}},
|
||||
author={Mehta, Dushyant and Sridhar, Srinath and Sotnychenko, Oleksandr and Rhodin, Helge and Shafiei, Mohammad and Seidel, Hans-Peter and Xu, Weipeng and Casas, Dan and Theobalt, Christian},
|
||||
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||||
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year={2017},
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publisher={ACM}
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||||
|
||||
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|
||||
author={Dimitrov, Svilen and Haas, Harald},
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||||
year={2015},
|
||||
publisher={Cambridge University Press}
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||||
}
|
||||
|
||||
@inproceedings{kuo2014luxapose,
|
||||
title={{Luxapose: Indoor Positioning with Mobile Phones and Visible Light}},
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||||
author={Kuo, Ye-Sheng and Pannuto, Pat and Hsiao, Ko-Jen and Dutta, Prabal},
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booktitle={Proceedings of the 20th Annual International Conference on Mobile Computing and Networking},
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year={2014},
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title={{A Step Counting Algorithm for Smartphone Users: Design and Implementation}},
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author={Pan, Meng-Shiuan and Lin, Hsueh-Wei},
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year={2015},
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||||
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title={{Activity Classification and Dead Reckoning for Pedestrian Navigation with Wearable Sensors}},
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author={Sun, Zuolei and Mao, Xuchu and Tian, Weifeng and Zhang, Xiangfen},
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IGNOREmonth={May}}
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@INPROCEEDINGS{Guo2016,
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IGNOREmonth={Oct}}
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title = {{Indoor Localization Competition}},
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note = {\url{http://evaal.aaloa.org/}, Accessed: 2018-09-22},
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IGNOREaddress = {{Rothenburg, Germany}},
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IGNOREmonth={Oct},}
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||||
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||||
|
||||
Reference in New Issue
Block a user