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@@ -1,53 +1,38 @@
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\section{Experiments}
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wir betrachten nur die fest-installierten APs die man meist anhand einer bestimmten mac-range ausmachen kann
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portable geraete von studenten, beamer, aehnliches werden ignoriert
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\todo{
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alles im FHWS gebäude [korrekte groesse fuer beide gebaeude!] mit nem nexus 6
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}
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Within all \docWIFI{} observations we only consider the \docAP{}s that are permanently installed
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within the building. Temporal and movable transmitters are ignored as they might cause estimation errors.
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modell direkt fuer den gelaufenen pfad optimiert (also wirklich jede wifi messung direkt auf den ground-truth)
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der fehler wird zwar kleiner, ist aber immernoch deutlich spürbar. das spricht dafür, dass das modell einfach nicht
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gut geeignet ist.
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outdoor fehler kann gemnindert werden mit z.B.
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nicht nur die APs nehmen die ich sehe, sondern auch die, die ich sehen müsste
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dann wird klar, dass es nicht gut passt. allerdings ist das auch gefaehrlich
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[nicht immer tauchen alle APs im scan auf] und welchen fehler bzw. welche
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dBm zahl nimmt man fuer fehlende APs an? das ist eine hardware-frage.
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ein workaround fuer steigende fehler durch optimierung koennte sein,
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dass man nicht jeden AP einzeln optimiert, sondern das gesamtsystem.
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und auch nicht den dB fehler, sondern 'die wahrscheinlichkeit an dieser stelle zu sein'
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bzw: 'die wahrscheinlichkeit aller anderen positionen minimieren' dass keine
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fehl-positionierungen [wie outdoor] mehr stattfinden. allerdings ist das
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problematisch da man auch hier entscheiden müsste wann ein AP nicht mehr
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sichtbar ist etc.
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walk1 hat eine issue kurz bevor man zur tuer zum hoersaalgebaude reingeht
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je nach resampling killt dieser wlan error evtl alle partikel!
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optimierungs input: alle 4 walks samt ground-truth
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dann kommt fuer die 4 typen [fixed, all same par, each par, each par pos]
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log probability 50 75, meter 50, 75
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reines wifi eval mittels num-opt springt stark durch die gegend
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d.h. das bewegungsmodell rettet uns
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kann man auch testen wenn man beim particle-filter das resampling ganz aus macht
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% -------------------------------- optimization -------------------------------- %
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As the signal strength prediction model is the heart of the absolute positioning component
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described in \ref{sec:system} we start with the model parameter estimation (see \ref{sec:optimization}) for
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\mTXP, \mPLE and \mWAF based on some reference measurements and compare the results
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between various optimization strategies and a basic empiric choice of \mTXP = \SI{-40}{\decibel{}m} @ \SI{1}{\meter}
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(defined by the usual \docAPshort{} transmit power for europe), a path loss exponent $\mPLE \approx $ \SI{2.5} and
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$\mWAF \approx$ \SI{-8}{\decibel} per floor/ceiling (made of reinforced concrete) \todo{cite für werte}.
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Figure \ref{fig:referenceMeasurements} depicts the location of the used 121 reference measurements.
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Each location was scanned 30 times ($\approx$ \SI{25}{\second} scan time),
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non permanent \docAP{}s were removed, the values were grouped per physical transmitter (see \ref{sec:vap})
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and aggregated to form the average signal strength per transmitter.
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% used reference measurements
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\begin{figure}
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{
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@@ -66,6 +51,24 @@ kann man auch testen wenn man beim particle-filter das resampling ganz aus macht
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% visible APs:
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% cnt(121) min(2.000000) max(22.000000) range(20.000000) med(8.000000) avg(9.322314) stdDev(4.386709)
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As mentioned earlier we will look at various optimization strategies.
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{\bf\noOptEmpiric{}} uses the same three parameters \mTXP,\mPLE,\mWAF for each \docAPshort{} in combination
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with its position, which is well known from the flooprlan.
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{\bf\optParamsAllAP{}} is the same as above, except that the three parameters are optimized
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based on the reference measurements.
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{\bf\optParamsEachAP{}} optimizes the three parameters per \docAP{} instead of using the same
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parameters for all.
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{\bf\optParamsPosEachAP{}} does not need any prior knowledge and will optimize all six parameters
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(3D position, \mTXP, \mPLE, \mWAF) based on the reference measurements.
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{\bf\optPerFloor{}} and {\bf\optPerRegion{}} are just like \optParamsPosEachAP{} except that
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there are several instances that are optimized only for one floor / region instead of the whole building.
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\begin{figure}
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\input{gfx/wifi_model_error_0_95.tex}
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\input{gfx/wifi_model_error_95_100.tex}
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@@ -215,10 +218,14 @@ kann man auch testen wenn man beim particle-filter das resampling ganz aus macht
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To reduce the amount such of misclassifications, where other locations within the building are
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as likely as the pedestrians actual location, we examined various approaches.
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Unfortunately, none of which provided a viable enhancement under all conditions for the performed walks:
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Unfortunately, none of which provided a viable enhancement under all conditions for the performed walks.
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One possibility to dissolve an equal \docWIFI{}-likelihood between two (or more) locations within in the building
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is, to not only consider the \docAPshort{}s seen by the Smartphone, but also the \docAPshort{}s not seen
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The misclassification-rate is determined by counting the amount of (random) locations within
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the building that produce a similar probability \refeq{eq:wifiProb} as the actual ground-truth
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position.
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One possibility to dissolve such an equal \docWIFI{}-likelihood between two (or more) locations is,
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to not only consider the \docAPshort{}s seen by the Smartphone, but also the \docAPshort{}s not seen
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by the Smartphone. This additional information can be used to rule out all locations where this
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\docAP{} should be received (high signal strength from the prediction model).
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% There might be an \docAP{} that should be visible at the other locations. However,
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@@ -234,20 +241,24 @@ kann man auch testen wenn man beim particle-filter das resampling ganz aus macht
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Furthermore, this requires the signal strength prediction model to be fairly accurate. Within our testing
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walks, several places are surrounded by concrete walls, which cause a harsh, local drop in signal strength.
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The models used within this work will not accurately predict the signal strength for such locations.
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Including \docAPshort{}s unseen by the Smartphone thus often increases the estimation error instead
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of fixing the multimodality.
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%%Including \docAPshort{}s unseen by the Smartphone thus often increases the estimation error instead
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%%of fixing the multimodality.
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To sum up, while some situations, e.g. outdoors, could greatly be improved,
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many other situations are deteriorated, especially when some transmitters are (temporarily)
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attenuated by ambient conditions like concrete walls.
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We therefore examined variations of the probability calculation from \refeq{eq:wifiProb}.
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Removing the strongest/weakest \docAPshort{} from $\mRssiVecWiFi{}$ yielded similar results.
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Removing the strongest/weakest \docAPshort{} from $\mRssiVec{}$ yielded similar results.
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While some estimations were improved, the overall estimation error increased for our walks,
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as there are many situations where only a handful \docAP{}s can be seen. Removing (valid)
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information will highly increase the error for such situations.
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Using a more strict exponential distribution for the model vs. scan comparison in \refeq{eq:wifiProb}
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had a positive effect on the misclassification error for some of the walks, but slightly increased
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the estimation error (see figure \reffig{fig:normalVsExponential}) and thus produced negative side effects.
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the estimation error (see figure \ref{fig:normalVsExponential}) and thus produced negative side effects.
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\begin{figure}
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\input{gfx/wifiCompare_normalVsExp_cross.tex}
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@@ -261,38 +272,19 @@ kann man auch testen wenn man beim particle-filter das resampling ganz aus macht
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}
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\end{figure}
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\todo{
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wir wollen nicht, dass die position des ground-truths durch das wifi so wahrscheinlich wie möglich ist,
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wir wollen dass die position des ground-truth einfach eine höhere wahrscheinlichkeit hat, als alle anderen punkte im gebäude
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das pruefen wir ab
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}
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\todo{
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erkenntnisse:
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schlechte messwerte (niedrige RSSI) aus der messung ignorieren hilft nur sehr sehr bedingt.. eher im gegenteil. meist geht der fehler (stark) hoch
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schlechteste messung weglassen ist auch schlecht
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sigma je nach signalstärke anpassen bringt leider auch nichts. wenn man das aber macht,
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erwähnen??? sigma je nach signalstärke anpassen bringt leider auch nichts. wenn man das aber macht,
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dann: fuer grosse signalstaerken ein grosses sigma! andersrum gehts nach hinten los!
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veto funktioniert auch nicht immer. es gibt stellen da ist ein AP wegen abschattung in der realität nicht sichtbar
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das smpartphone sieht ihn deshalb nicht, im model ist er aber fälschlicherweise da deshalb falsches veto
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oder das smartphone sieht einen AP wegen kollisionen nicht oder weil er durch den rücken stark verdeckt wird
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es gibt einfach stellen an denen das wifi nicht eindeutig ist, die an anderen stellen quasi exakt genauso vorliegen
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da laesst sich dann nicht viel machen
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}
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% -------------------------------- final system -------------------------------- %
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% REAL WALKS
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\todo{obwohl das angepasste modell doch recht gut laeuft und der fehler recht klein wird, sind immernoch stellen dabei,
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wo es einfach nicht gut passt, unguenstige mehrdeutigkeiten vorliegen, oder regionen einfach nicht passen wie sie sollten.
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@@ -302,6 +294,10 @@ kann man auch testen wenn man beim particle-filter das resampling ganz aus macht
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\todo{GPS ist leider kaum eine hilfe. entweder kein empfang wegen ueberdachung oder abschattung, oder
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zu kurz draußen um einen guten gps-fix zu bekommen.}
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\todo{
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walk1 hat eine issue kurz bevor man zur tuer zum hoersaalgebaude reingeht
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je nach resampling killt dieser wlan error evtl alle partikel!
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}
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\todo{
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das bbox modell hat probleme an den uebergängen zwischen bboxes da dort teils starke spruenge sind
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@@ -1,4 +1,5 @@
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\section{Indoor Positioning System}
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\label{sec:system}
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Our smartphone-based indoor localization system estimates the current location and heading
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using recursive density estimation.
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@@ -1,4 +1,5 @@
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\section{WiFi Optimization}
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\section{WiFi Location Estimation}
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\label{sec:optimization}
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The WiFi sensor infers the pedestrian's current location based on a comparison between live measurements
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(the smartphone continuously scans for nearby \docAP{}s) and reference measurements / predictions
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@@ -146,17 +147,46 @@
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\subsection{Modified Signal Strength Model}
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\todo{nicht: during initial eval, sondern gleich sagen, dass die vermutung nahe liegt, dass das modell
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nicht gut klappen wird, weil waende und unser metall-glas nicht beruecksichtigt werden. deshalb
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versuchen wir ein anderes modell das immernoch live arbeiten kann}
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During the initial eval, some issues were discovered. While aforementioned optimization was able to
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reduce the error between reference measurements and model estimations to \SI{50}{\percent},
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the position estimation \ref{eq:wifiProb} did not benefit from improved model parameters.
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To the contrary, there were several situations throughout the testing walks, where
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the inferred location was more erroneous than before.
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%\todo{nicht: during initial eval, sondern gleich sagen, dass die vermutung nahe liegt, dass das modell
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%nicht gut klappen wird, weil waende und unser metall-glas nicht beruecksichtigt werden. deshalb
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%versuchen wir ein anderes modell das immernoch live arbeiten kann}
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%During the initial eval, some issues were discovered. While aforementioned optimization was able to
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%reduce the error between reference measurements and model estimations to \SI{50}{\percent},
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%the position estimation \ref{eq:wifiProb} did not benefit from improved model parameters.
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%To the contrary, there were several situations throughout the testing walks, where
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%the inferred location was more erroneous than before.
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As the used model does not consider walls, it is expected to provide erroneous values
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for regions that are heavily attenuated by e.g. concrete or metallised glass.
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\subsection{\docWIFI{} quality factor}
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\todo{wifi quality factor??}
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\todo{formel für toni}
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\begin{equation}
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\newcommand{\leMin}{l_\text{min}}
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\newcommand{\leMax}{l_\text{max}}
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q(\mRssiVec) =
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\max(0,
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\min(
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\frac{
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\bar\mRssi - \leMin
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}{
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\leMax - \leMin
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},
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1
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)
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)
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\label{eq:wifiQuality}
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\end{equation}
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\subsection {VAP grouping}
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\label{sec:vap}
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Assuming normal conditions, the received signal strength at one location will (strongly) vary
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due to environmental conditions like temperature, humidity, open/closed doors, RF interference.
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