worked on introduction, realted-work and system
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\section{Recursive State Estimation}
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We consider indoor localisation as a time-sequential, non-linear and non-Gaussian state estimation problem.
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Using a recursive Bayes filter that satisfies the Markov property, the posterior distribution at time $t$ can be written as
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%
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\begin{equation}
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\arraycolsep=1.2pt
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\begin{array}{ll}
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&p(\mStateVec_{t} \mid \mObsVec_{1:t}) \propto\\
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&\underbrace{p(\mObsVec_{t} \mid \mStateVec_{t})}_{\text{evaluation}}
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\int \underbrace{p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t})}_{\text{transition}}
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\underbrace{p(\mStateVec_{t-1} \mid \mObsVec_{1:t-1})d\vec{q}_{t-1}}_{\text{recursion}} \enspace,
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\end{array}
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\label{equ:bayesInt}
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\end{equation}
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%
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where $\mObsVec_{1:t} = \mObsVec_{1}, \mObsVec_{1}, ..., \mObsVec_{t}$ is a series of observations up to time $t$.
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The hidden state $\mStateVec$ is given by
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\begin{equation}
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\mStateVec = (x, y, z, \mObsHeading, \mStatePressure),\enskip
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x,y,z,\mStatePressure \in \R \enspace,
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\end{equation}
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where $x, y, z$ represent the 3D position, $\mObsHeading$ the user's heading and $\mStatePressure$ the relative pressure prediction in hectopascal (hPa).
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The recursive part of the density estimation contains all information up to time $t$.
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Further, the state transition models the pedestrian’s movement based upon random walks on graphs, which will be described in section \ref{sec:trans}.
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It should be noted, that we also include the current observation $\mObsVec_{t}$ in it.
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We consider indoor localisation as a time-sequential, non-linear and non-Gaussian state estimation problem.
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Using a recursive Bayes filter that satisfies the Markov property, the posterior distribution at time $t$ can be written as
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%
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\begin{equation}
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\arraycolsep=1.2pt
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\begin{array}{ll}
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&p(\mStateVec_{t} \mid \mObsVec_{1:t}) \propto\\
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&\underbrace{p(\mObsVec_{t} \mid \mStateVec_{t})}_{\text{evaluation}}
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\int \underbrace{p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t})}_{\text{transition}}
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\underbrace{p(\mStateVec_{t-1} \mid \mObsVec_{1:t-1})d\vec{q}_{t-1}}_{\text{recursion}} \enspace,
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\end{array}
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\label{equ:bayesInt}
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\end{equation}
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%
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where $\mObsVec_{1:t} = \mObsVec_{1}, \mObsVec_{1}, ..., \mObsVec_{t}$ is a series of observations up to time $t$.
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The hidden state $\mStateVec$ is given by
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\begin{equation}
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\mStateVec = (x, y, z, \mObsHeading, \mStatePressure),\enskip
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x, y, z, \mObsHeading \mStatePressure \in \R \enspace,
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\end{equation}
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%
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where $x, y, z$ represent the position in 3D space, $\mObsHeading$ the user's heading and $\mStatePressure$ the
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relative pressure prediction in hectopascal (hPa).
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The recursive part of the density estimation contains all information up to time $t$.
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Furthermore, the state transition models the pedestrian's movement based on random walks on graphs,
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described in section \ref{sec:trans}.
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%It should be noted, that we also include the current observation $\mObsVec_{t}$ in it.
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Differing from the usual notation, the state transition also includes the current observation $\mObsVec_{t}$.
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\commentByFrank{brauchen wir hier noch das cite?}
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Containing all relevant sensor measurements to evaluate the current state, the observation vector is defined as follows:
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%
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\begin{equation}
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\mObsVec = (\mRssiVec_\text{wifi}, \mRssiVec_\text{ib}, \mObsHeading, \mObsSteps, \mObsPressure) \enspace,
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\end{equation}
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%
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where $\mRssiVec_\text{wifi}$ is the Wi-Fi and $\mRssiVec_\text{ib}$ the iBeacon signal strength vector.
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The information, if a step or turn was detected, is given as a Boolean value.
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\commentByToni{Wie sieht die Observation nun genau aus? Fehlt da nicht Step und Turn?}
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Finally, $\mObsPressure$ is the relative barometric pressure with respect to some fixed point in time.
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For incorporating the highly different sensor types, one should refer to the process of probabilistic sensor fusion \cite{Khaleghi2013}.
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By assuming statistical independence of all sensor models, the probability density of the state evaluation is given by
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%
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\begin{equation}
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\begin{split}
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&p(\vec{o}_t \mid \vec{q}_t, \vec{q}_{t-1}) = \\
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&p(\vec{o}_t \mid \vec{q}_t)_\text{baro}
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\,p(\vec{o}_t \mid \vec{q}_t)_\text{ib}
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\,p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}
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\end{split} \enspace.
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\label{eq:evalBayes}
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\end{equation}
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%
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Here, every single component refers to a probabilistic sensor model.
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The barometer information is evaluated using $p(\vec{o}_t \mid \vec{q}_t)_\text{baro}$, whereby absolute position information is given by $p(\vec{o}_t \mid \vec{q}_t)_\text{ib}$ for iBeacons and by $p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}$ for Wi-Fi.
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Containing all relevant sensor measurements to evaluate the current state, the observation vector is defined as follows:
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%
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\begin{equation}
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\mObsVec = (\mRssiVec_\text{wifi}, \mRssiVec_\text{ib}, \mObsHeading, \mObsSteps, \mObsPressure) \enspace,
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\end{equation}
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%
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where $\mRssiVec_\text{wifi}$ and $\mRssiVec_\text{ib}$ contain the measurements of all nearby \docAP{}s (\docAPshort{})
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and \docIBeacon{}s, respectively. $\mObsHeading$ and $\mObsSteps$ describe the relative angular change and the number
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of steps detected for the pedestrian.
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Finally, $\mObsPressure$ is the relative barometric pressure with respect to some fixed point in time.
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For further information on how to incorporate such highly different sensor types,
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one should refer to the process of probabilistic sensor fusion \cite{Khaleghi2013}.
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By assuming statistical independence of all sensors, the probability density of the state evaluation is given by
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%
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\begin{equation}
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%\begin{split}
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p(\vec{o}_t \mid \vec{q}_t) =
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p(\vec{o}_t \mid \vec{q}_t)_\text{baro}
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\,p(\vec{o}_t \mid \vec{q}_t)_\text{ib}
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\,p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}
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\enspace.
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%\end{split}
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\label{eq:evalBayes}
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\end{equation}
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%
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Here, every single component refers to a probabilistic sensor model.
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The barometer information is evaluated using $p(\vec{o}_t \mid \vec{q}_t)_\text{baro}$,
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whereby absolute position information is given by $p(\vec{o}_t \mid \vec{q}_t)_\text{ib}$ for
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\docIBeacon{}s and by $p(\vec{o}_t \mid \vec{q}_t)_\text{wifi}$ for \docWIFI{}.
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It is well known that finding analytic solutions for densities is very difficult and they only exit in rare cases.
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Therefore, numerical solutions like Gaussian filters or the broad class of Monte Carlo methods are deployed \cite{sarkka2013bayesian}.
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Since we assume that indoor localisation is a time-sequential, non-linear and non-Gaussian process, a particle filter for approximating the posterior distribution is chosen.
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Within this work the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t})$ is used as proposal distribution, what is also known as CONDENSATION algorithm \cite{Isard98:CCD}.
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It is well known that finding analytic solutions for densities is very difficult and only possible in rare cases.
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Therefore, numerical solutions like Gaussian filters or the broad class of Monte Carlo methods are deployed \cite{sarkka2013bayesian}.
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Since we assume indoor localisation to be a time-sequential, non-linear and non-Gaussian process,
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a particle filter is chosen as approximation of the posterior distribution.
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Within this work the state transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t})$ is used as proposal distribution,
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what is also known as CONDENSATION algorithm \cite{Isard98:CCD}.
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\commentByFrank{caps? fehlt da noch was?}
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