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

first draft related work

Browse Source
This commit is contained in:
toni
2016-04-19 18:28:30 +02:00
parent 978ffd89a5
commit 033466111a
5 changed files with 176 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

BIN
tex/bare_conf.dvi
View File

Binary file not shown.

111
tex/chapters/experiments.txt Normal file
View File

@@ -0,0 +1,111 @@
bergwerk_nexus4
path1_simple
cnt(170) min(124.621) max(1211.95) range(1087.33) med(494.071) avg(572.633) stdDev(290.127)
path1_importance
cnt(170) min(110.992) max(1223.18) range(1112.18) med(482.417) avg(545.237) stdDev(281.696)
cnt(170) min(90.0697) max(1231.7) range(1141.63) med(447.12) avg(539.853) stdDev(291.265)
path1_multi
cnt(170) min(19.3289) max(1323.02) range(1303.69) med(293.464) avg(401.001) stdDev(348.936)
path1_shortest
cnt(170) min(51.5347) max(1303.31) range(1251.78) med(296.367) avg(410.459) stdDev(356.167)
path2_simple
cnt(205) min(32.1503) max(1154.48) range(1122.33) med(379.831) avg(440.884) stdDev(316.976)
path2_importance
cnt(205) min(29.756) max(1182.57) range(1152.81) med(338.978) avg(446.471) stdDev(326.851)
path2_multi
cnt(205) min(26.3266) max(903.358) range(877.031) med(264.985) avg(334.287) stdDev(246.188)
path2_shortest
cnt(205) min(3.15803) max(1003.9) range(1000.74) med(261.279) avg(355.759) stdDev(280.638)
path3_simple
cnt(177) min(27.9775) max(1023.35) range(995.376) med(396.515) avg(461.918) stdDev(292.705)
path3_importance
cnt(177) min(22.4515) max(1044.03) range(1021.58) med(374.274) avg(461.38) stdDev(309.641)
path3_multi
cnt(177) min(7.19039) max(1057.11) range(1049.92) med(327.144) avg(445.415) stdDev(311.683)
path3_shortest
cnt(177) min(19.0567) max(1063.02) range(1043.96) med(274.569) avg(409.897) stdDev(346.419)
path4_simple
cnt(291) min(24.7635) max(1299.99) range(1275.23) med(401.258) avg(446.048) stdDev(311.301)
path4_importance
cnt(291) min(5.35469) max(1299.99) range(1294.63) med(412.518) avg(451.873) stdDev(323.578) // treppe gegen ende erhoeht fehler
path4_multi
cnt(291) min(24.8386) max(1306.86) range(1282.02) med(372.959) avg(457.002) stdDev(319.193)
path4_single
cnt(291) min(43.2627) max(1307.2) range(1263.94) med(328.561) avg(439.296) stdDev(323.685)
bergwerk_nexus_no_offset (ohne das fake 750 ms offset)
path1_simple
cnt(170) min(169.656) max(1262.86) range(1093.2) med(608.042) avg(651.252) stdDev(280.247)
path1_importance
cnt(170) min(152.098) max(1261.35) range(1109.26) med(583.717) avg(608.73) stdDev(284.415)
path1_multi
cnt(170) min(17.3608) max(1297.59) range(1280.23) med(315.801) avg(408.499) stdDev(342.586)
path1_shortest
cnt(170) min(9.42833) max(1341.19) range(1331.76) med(283.329) avg(387.529) stdDev(360.54)
path2_simple
cnt(205) min(15.7351) max(1180.82) range(1165.09) med(385.005) avg(465.017) stdDev(338.186)
path2_importance
cnt(205) min(10.5153) max(1144.36) range(1133.85) med(389.13) avg(455.443) stdDev(327.471)
path2_multi
cnt(205) min(16.1751) max(962.944) range(946.769) med(232.685) avg(343.799) stdDev(270.153)
path2_shortest
cnt(205) min(3.725) max(984.496) range(980.771) med(276.364) avg(374.586) stdDev(300.294)
path3_simple
cnt(177) min(27.9435) max(996.607) range(968.664) med(458.031) avg(475.898) stdDev(284.424)
path3_importance
cnt(177) min(23.7427) max(975.215) range(951.472) med(462.579) avg(484.764) stdDev(289.334)
path3_multi
cnt(177) min(23.928) max(1092.71) range(1068.79) med(278.461) avg(444.438) stdDev(329.607)
path3_shortest
cnt(177) min(9.26234) max(1068.06) range(1058.8) med(266.055) avg(404.565) stdDev(346.63)
path4_simple
cnt(291) min(13.683) max(1298.77) range(1285.09) med(417.002) avg(468.686) stdDev(330.651)
path4_importance
cnt(291) min(13.759) max(1347.91) range(1334.15) med(467.665) avg(498.666) stdDev(313.775)
path4_multi
cnt(291) min(8.56751) max(1315.14) range(1306.57) med(347.575) avg(428.311) stdDev(342.233)
path4_shortest
cnt(291) min(19.3812) max(1358.26) range(1338.88) med(358.279) avg(426.838) stdDev(321.88)
bergwerk_galaxy_no_offset (ohne das fake 750 ms offset)
path1_simple
cnt(171) min(44.5758) max(2008.8) range(1964.22) med(1023.35) avg(961.773) stdDev(370.492) // huge wifi and barometer issues
path1_multi
cnt(171) min(85.9269) max(2157.4) range(2071.47) med(613.383) avg(705.732) stdDev(467.525) // much better, high initial error -> even better
path1_shortest
cnt(171) min(64.679) max(2185.99) range(2121.31) med(639.352) avg(746.264) stdDev(462.957) // similar: much better
path2_simple
cnt(193) min(31.4961) max(1596.12) range(1564.62) med(671.196) avg(714.763) stdDev(447.28)
path2_multi
cnt(193) min(2.47806) max(1447.66) range(1445.18) med(440.189) avg(500.696) stdDev(381.872)
path2_shortest
cnt(193) min(13.2449) max(1453.03) range(1439.78) med(449.287) avg(532.14) stdDev(402.977)
path3_simple
cnt(158) min(36.0375) max(1863.92) range(1827.88) med(615.248) avg(678.632) stdDev(428.826) // short, stairs only, bad baromter, nothing to optimize
path3_multi
cnt(158) min(26.7108) max(1610.37) range(1583.66) med(562.175) avg(582.494) stdDev(382.947) // only slightly better
path3_shortest
cnt(158) min(39.0438) max(1778.07) range(1739.03) med(587.44) avg(648.729) stdDev(374.347) // only slightly better
path4_simple
cnt(293) min(32.1262) max(1875.25) range(1843.13) med(889.001) avg(836.58) stdDev(484.112)
path4_multi
cnt(293) min(17.1514) max(1686.81) range(1669.66) med(620.385) avg(702.897) stdDev(446.311)
path4_shortest
cnt(293) min(17.6632) max(1740.36) range(1722.7) med(732.178) avg(778.587) stdDev(438.974) // die treppe am ende ist problematisch

31
tex/chapters/relatedwork.tex
View File

@@ -29,14 +29,33 @@ In recent years, smoothing gets attention mainly in other areas as indoor locali
The early work of \cite{isard1998smoothing} demonstrates the possibilities of smoothing for visual tracking.
They used a combination of the CONDENSATION particle filter with a forward-backward smoother.
Based on this pioneering approach, many different solutions for visual and multi-target tracking have been developed \cite{Perez2004}.
For example, in \cite{Platzer:2008} a particle smoother is used to reduce multimodalities in a blood flow simulation for human vessels. Or \cite{}
Nevertheless, their are some promising approach for indoor localisation systems as well. For example ...
For example, in \cite{Platzer:2008} a particle smoother is used to reduce multimodalities in a blood flow simulation for human vessels. Or \cite{Hu2014} uses a smoother to overcoming the problem of particle impoverishment while predicting the Remaining Useful Life (RUL) of equipment (e.g. a Lithium-ion battery).
%smoothing im bezug auf indoor
Smoothing solutions in indoor localisation werden bisher nicht wirklich behandelt. das liegt hauptsächlich daran das es sehr langsam ist \cite{}. es gibt ansätze von ... und ... diese benutzen blah und blah. wir machen das genauso/besser.
Nevertheless, their are some promising approaches for indoor localisation systems as well.
For example \cite{Nurminen2014} deployed a fixed-interval forward-backward smoother to improve the position estimation for non-real-time applications.
They combined Wi-Fi, step and turn detection, a simple line-of-sight model for floor plan restrictions and the barometric change within a particle filter.
The state transition samples a new state based on the heading change, altitude change and a fixed step length.
The experiments of \cite{Nurminen2014} clearly emphasize the benefits of smoothing techniques. The estimation error could be decreased significantly.
However, a fixed-lag smoother was treated only in theory.
In the work of \cite{Paul2009} both fixed-interval and fixed-lag smoothing were presented.
They implemented Wi-Fi, binary infra-red motion sensors, binary foot-switches and a potential field for floor plan restrictions.
Those sensors were incorporated using a sigma-point Kalman filter in combination with a forward-backward smoother.
It was also proven by \cite{Paul2009}, that the fixed-lag smoother is slightly less accurate then the fixed-interval smoother.
As one would expect from the theoretical foundation.
Unfortunately, even a sigma-point Kalman filters is after all just a linearisation and therefore not as flexible and suited for the complex problem of indoor localisation as a non-linear estimator like a particle filter.
\commentByToni{Kann das jemand nochmal verifizieren? Das mit dem Kalman Filter. Danke.}
Additionally, the Wi-Fi RSSI model requires known calibration points and is deployed using a remarkable number of access points for very small spaces.
In our opinion this is not practical and we would further recommend adding a PDR-based transition instead of a random one.
In contrast, the here presented approach is able to use two different smoothing algorithm, both implemented as fixed-interval and fixed-lag versions.
Further, our localisation system presented in \cite{Ebner-16} enables us to walk stairs and therefore going into the third dimension.
Therefore, a regularly tessellated graph is utilized to avoid walls, detecting doors and recognizing stairs.
Within this work, this is additionally supported by a simple classification that detects the activities unknown, standing, walking and walking stairs.
Finally, we incorporate prior navigation knowledge by using syntactically calculated realistic human walking paths \cite{Ebner-16}.
This method makes use of the given destination and thereby provides a more targeted movement.

8
tex/chapters/smoothing.tex
View File

@@ -2,3 +2,11 @@
\label{sec:smoothing}
Consider a situation given all observations until a time step T...
%komplexität eingehen
The reason for not behandeln liegt ...
However, \cite{} and \cite{} have proven this wrong and reduced the complexity of different smoothing methods.

33
tex/egbib.bib
View File

@@ -2718,9 +2718,40 @@ number = {3},
pages = {495--513},
shorttitle = {Proceedings of the IEEE},
title = {{Data Fusion for Visual Tracking with Particles}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1271403},
volume = {92},
year = {2004}
}
@article{Hu2014,
abstract = {This work addresses the problem of predicting the Remaining Useful Life (RUL) of components for which a mathematical model describing the component degradation is available, but the values of the model parameters are not known and the observations of degradation trajectories in similar components are unavailable. The proposed approach solves this problem by using a Particle Filtering (PF) technique combined with a kernel smoothing (KS) method. This PF-KS method can simultaneously estimate the degradation state and the unknown parameters in the degradation model, while significantly overcoming the problem of particle impoverishment. Based on the updated degradation model (where the unknown parameters are replaced by the estimated ones), the RUL prediction is then performed by simulating future particles evolutions. A numerical application regarding prognostics for Lithium-ion batteries is considered. Various performance indicators measuring precision, accuracy, steadiness and risk of the obtained RUL predictions are computed. The obtained results show that the proposed PF-KS method can provide more satisfactory results than the traditional PF methods.},
author = {Hu, Yang and Baraldi, Piero and {Di Maio}, Francesco and Zio, Enrico},
doi = {10.1016/j.ress.2014.10.003},
file = {:home/toni/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hu et al. - 2015 - A particle filtering and kernel smoothing-based approach for new design component prognostics.pdf:pdf},
issn = {09518320},
journal = {Reliability Engineering and System Safety},
keywords = {Battery,Kernel smoothing,Parameter estimation,Particle filtering,Prognostics,Remaining useful life},
IGNOREmonth = {feb},
pages = {19--31},
title = {{A Particle filtering and Kernel Smoothing-Based Approach for New Design Component Prognostics}},
volume = {134},
year = {2014}
}
@article{Paul2009,
abstract = {Solutions for indoor tracking and localization have become more critical with recent advancement in context and location-aware technologies. The accuracy of explicit positioning sensors such as global positioning system (GPS) is often limited for indoor environments. In this paper, we evaluate the feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available. This paper proposes a sigma-point Kalman smoother (SPKS)-based location and tracking algorithm as a superior alternative for indoor positioning. The proposed SPKS fuses a dynamic model of human walking with a number of low-cost sensor observations to track 2-D position and velocity. Available sensors include Wi-Fi received signal strength indication (RSSI), binary infra-red (IR) motion sensors, and binary foot-switches. Wi-Fi signal strength is measured using a receiver tag developed by Ekahau, Inc. The performance of the proposed algorithm is compared with a commercially available positioning engine, also developed by Ekahau, Inc. The superior accuracy of our approach over a number of trials is demonstrated.},
author = {Paul, Anindya S. and Wan, Eric A.},
doi = {10.1109/JSTSP.2009.2032309},
isbn = {1932-4553},
issn = {19324553},
journal = {IEEE Journal on Selected Topics in Signal Processing},
keywords = {Bayesian inference,Indoor tracking,Received signal strength indication (RSSI)-based localization,Sigma-point Kalman filter,Sigma-point Kalman smoother,State estimation},
IGNOREmonth = {oct},
number = {5},
pages = {860--873},
shorttitle = {IEEE Journal of Selected Topics in Signal Processi},
title = {{RSSI-Based Indoor Localization and Tracking using Sigma-Point Kalman Smoothers}},
volume = {3},
year = {2009}
}