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first draft related work

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toni
2016-04-19 18:28:30 +02:00
parent 978ffd89a5
commit 033466111a
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111
tex/chapters/experiments.txt Normal file
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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

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tex/chapters/relatedwork.tex
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@@ -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.

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tex/chapters/smoothing.tex
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@@ -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.