updated bib, introduction and ftm part

tex/chapters/1_introduction.tex

@@ -5,12 +5,36 @@ Many indoor positing system (IPS) rely on radio technologies for estimating a pe
 
 %Probleme von RSSI
 By measuring the RSSI emitted from Wi-Fi access points (AP) at known locations, the receiver and thus the pedestrian is able to get its current position using the principles of multilateration. 
-Here, at least three APs are needed for two-dimensional positioning and at least four APs are needed for three-dimensional positioning \cite{}. 
+Here, at least three APs are needed for two-dimensional positioning and at least four APs are needed for three-dimensional positioning. 
-However, as a simple deterministic RSSI-based multilaterian is very prone to errors in real world scenarios, causing unacceptable inaccuracies, it is necessary to add more advanced methods to approach the positioning problem in a more accurate and stable way.
+However, as a simple RSSI-based multilaterian is very prone to errors in real world scenarios, causing unacceptable inaccuracies, it is necessary to add more advanced methods to approach the positioning problem in a more accurate and stable way.
-The two most popular are fingerprinting and signal strength prediction \cite{}. 
+The two most popular are fingerprinting and signal strength prediction \cite{davidson2017survey, Afyouni2012}. 
 
-Fingerprinting... very accurate, very advanced methods, ap positions müssen nicht bekannt sein... however, high setup time, ständiges neu anlegen der fingerprints, schwierig wenn viele menschen im gebäude sind, weil sich dann die fingerprints stark ändern. 
+Fingerprinting is the process of taking RSSI measurements at known positions distributed throughout the building in an so called offline or recording phase. 
-In Contrast prediction only needs ap positions und physikalisches model wie log distance. modell relys on many parameters, hard to receive. however provides also good results with only a few parameters, not so accurate then fingerprinting but without large setup time..
+These fingerprints can then be used in the online or localization phase to obtain the current position by finding the closest match, e.g. by using a nearest neighbour search.
+Given the current RSSI value the most likely position is the one which is closest to other similar RSSI fingerprints.
+This method includes the characteristics of the environment into the prerecorded fingerprints.
+Thus, the structure of the building (e.g. walls and furniture) and the positions of the access points need not necessarily be known.
+In principle, very small positioning errors in the lower single-digit metre range can be achieved by using this method.
+However, the accuracy depends heavily on the density of the fingerprints and whether the smartphone used for the recordings will also be used later for the localization, as the RSSI characteristics can highly differ between single devices \cite{he2016wi}.
+Recording the fingerprints is a time-consuming and tedious process for large buildings and needs to be redone whenever the environment changes significantly.
+
+In contrast, signal strength prediction methods determine signal strengths for arbitrary locations by using an estimation model instead of real measurements.
+This makes an offline phase unnecessary and only the positions of the access points have to be known as well as some other model parameters.
+However, complex scenarios with many non-line-of-sight (NLOS) transmissions require an equally complex signal strength prediction model, taking into account the characteristics of the environment like the different attenuation coefficients of the building's walls \cite{PathLossPredictionModelsForIndoor, WithoutThePain}.
+The determination of such parameters is often only possible with a great deal of effort and material knowledge, especially for older buildings \cite{Fetzer-18}. 
+Furthermore, to determine a more or less realistic signal prediction for a location in question, intersections checks of each obstacle within the line-of-sight to the AP have to be calculated, which is costly for larger buildings. 
+For this reason, simplified models are often used for the calculation, which do without intersection checks and precisely determined parameters. 
+The accuracies achieved in this case are often in a reasonable medium single-digit range of meters \cite{Ebner-17, farid2013recent}.
+
+As both, fingerprinting and signal strength prediction, are based on RSSI readings, the accuracy is affected by similar factors.
+For example, temporary and unpredictable occurrences such as large crowds are very difficult to compensate, as the signals are highly attenuated by the human body. 
+Without additional sources like cameras, there can be no indication where and how many people are in an area, what makes it hard to adapt the above discussed methods accordingly.
+
+
+
+higher accuracy with more information about the environment.. walls etc 
+
+ap positions und physikalisches model wie log distance. modell relys on many parameters, hard to receive. however provides also good results with only a few parameters, not so accurate then fingerprinting but without large setup time..
 
 -> großer auftritt FTM
 

tex/chapters/3_ftm.tex

@@ -83,11 +83,11 @@ In order to take walls into account the model must include the power loss of eve
 Often the dampening factors of walls are unknown and hard to measure.
 Additionally, the computation of the wall-attenuation factor model requires costly intersection tests with the geometry of the environment which can be intractable to perform on a regular smartphone.
 
-Another approach is to take measurements at known positions distributed throughout the building.
+%Another approach is to take measurements at known positions distributed throughout the building.
-These fingerprints can then be used in the localization phase to obtain the current position with a nearest neighbour search.
+%These fingerprints can then be used in the localization phase to obtain the current position with a nearest neighbour search.
-Given the current RSSI value the most likely position is the one which is closest to other similar RSSI fingerprints.
+%Given the current RSSI value the most likely position is the one which is closest to other similar RSSI fingerprints.
-This method includes the characteristics of the environment into the prerecorded fingerprints.
+%This method includes the characteristics of the environment into the prerecorded fingerprints.
-Recording the fingerprints is a time-consuming and tedious process for large buildings and needs to be redone whenever the environment changes significantly.
+%Recording the fingerprints is a time-consuming and tedious process for large buildings and needs to be redone whenever the environment changes significantly.
  
 %While fingerprinting can increase the accuracy of the localization computing the distance from the RSSI directly is easier to deploy.
  

tex/egbib.bib

@@ -2974,6 +2974,722 @@ address = {{Rothenburg, Germany}},
   publisher={IEEE}
 }
 
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+	year={2011},
+	REM_volume={},
+	REM_number={},
+	pages={3526-3532},
+	REM_keywords={mesh generation;navigation;virtual reality;navigation meshes;realistic multilayered environments;virtual characters;path planning;airport;multistorey building;two-dimensional polygons;Navigation;Data structures;Complexity theory;Buildings;Airports;Path planning;Graphics},
+	REM_doi={10.1109/IROS.2011.6094790},
+	REM_ISSN={2153-0866},
+	REM_month={Sept},
+}
+
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+
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+
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+  journal={Sensors},
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+
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+}
+
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+}
+
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+  year={2011}
+}
+
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+  title={{Wireless RSSI Fingerprinting Localization}},
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+
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+}
+
+@inproceedings{yeh2009indoor,
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+  year={2009},
+  organization={IEEE}
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+  year={2017},
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+}
+
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+
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+
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+
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+}
+
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+  journal={GetMobile: Mobile Computing and Communications},
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+  year={2015},
+  publisher={ACM}
+}
+
+@article{lymberopoulos2017microsoft,
+  title={{Microsoft Indoor Localization Competition: Experiences and Lessons Learned}},
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+  organization={IEEE}
+}
+
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+}
+
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+
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+
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+
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+  organization={IEEE}
+}
+
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+  pages={329--342},
+  year={2014},
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+}
+
+@INPROCEEDINGS{Solin2016,
+author={A. Solin and S. Särkkä and J. Kannala and E. Rahtu},
+booktitle={2016 European Navigation Conference (ENC)},
+title={{Terrain Navigation in the Magnetic Landscape: Particle Filtering for Indoor Positioning}},
+year={2016},
+volume={},
+number={},
+pages={1-9},
+ISSN={},
+IGNOREmonth={May}}
+
+@INPROCEEDINGS{Guo2016,
+author={X. Guo and W. Shao and F. Zhao and Q. Wang and D. Li and H. Luo},
+booktitle={2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
+title={{WiMag: Multimode Fusion Localization System based on Magnetic/WiFi/PDR}},
+year={2016},
+volume={},
+number={},
+pages={1-8},
+ISSN={2471-917X},
+IGNOREmonth={Oct}}
+
+@misc{EvAAl,
+author = {{Evaluating AAL Systems through Competitive Benchmarking}},
+title = {{Indoor Localization Competition}},
+note = {\url{http://evaal.aaloa.org/}, Accessed: 2018-09-22},
+IGNOREaddress = {{Rothenburg, Germany}},
+}
+
+@inproceedings{jaworski2017real,
+  title={{Real-Time 3D Indoor Localization}},
+  author={Jaworski, Wojciech and Wilk, Pawe{\l} and Zborowski, Pawe{\l} and Chmielowiec, Witold and Lee, Andrew YongGwon and Kumar, Abhishek},
+  booktitle={Indoor Positioning and Indoor Navigation (IPIN), 2017 International Conference on},
+  pages={1--8},
+  year={2017},
+  organization={IEEE}
+}
+
+@inproceedings{moschevikin2013realtrac,
+  title={{Realtrac Technology Overview}},
+  author={Moschevikin, Alex and Galov, Aleksandr and Soloviev, Alexei and Mikov, Alexander and Volkov, Alexander and Reginya, Sergey},
+  booktitle={International Competition on Evaluating AAL Systems through Competitive Benchmarking},
+  pages={60--71},
+  year={2013},
+  organization={Springer}
+}
+
+@inproceedings{klepal2008backtracking,
+  title={{A Backtracking Particle Filter for Fusing Building Plans with PDR Displacement Estimates}},
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+  year={2008},
+  organization={IEEE}
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+JOURNAL = {Sensors},
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+NUMBER = {10},
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+JOURNAL = {Sensors},
+VOLUME = {18},
+YEAR = {2018},
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+DOI = {10.3390/s18124095}
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 @inproceedings{ibrahim2018verification,
   title={Verification: Accuracy evaluation of WiFi fine time measurements on an open platform},
@@ -3013,4 +3729,4 @@ address = {{Rothenburg, Germany}},
   pages={13--20},
   year={2014},
   organization={ACM}
-}
+}