alpha immpf part

tex/chapters/method.tex

@@ -131,15 +131,26 @@ However, it is obvious that \eqref{equ:KLD} only works reliable if the measureme
 Especially Wi-Fi serves as the main source for estimation and thus attenuated or bad Wi-Fi readings are causing $D_{\text{KL}}$ to grow, even if the dominant filter provides a good position estimation. 
 In such scenarios a lower diversity and higher focus of the particle set, as given by the dominant filter, is required.
 We achieves this by introducing a Wi-Fi quality factor, allowing the support filter to pick particles from the dominant filter and prevent the later from doing it vice versa.
-The quality factor $ $ is defined by
-	%
-	\begin{equation}
-	d
-	\enspace ,
-	\label{equ:immpWifiQuality}
-	\end{equation}
-	%
-where..
+The quality factor is defined by
+		%
+		\begin{equation}
+            \newcommand{\leMin}{l_\text{min}}
+            \newcommand{\leMax}{l_\text{max}}
+            q(\mObsVec_t^{\mRssiVec_\text{wifi}}) = 
+                \max(0,
+                    \min(
+                        \frac{
+                            \bar\mRssi_\text{wifi} - \leMin
+                        }{
+                            \leMax - \leMin
+                        },
+                        1
+                    )
+                )
+            \label{eq:wifiQuality}
+        \end{equation}
+		%
+where $\bar\mRssi_\text{wifi}$ is the average of all signal-strength measurements received from the observation $\mObsVec_t$. An upper and lower bound is given by $l_\text{max}$ and $l_\text{min}$. 
 
 
 To incorporate all this within the IMMPF, we utilize a non-trivial Markov switching process.
@@ -151,7 +162,7 @@ Considering the above presented measures, $\Pi_t$ is two-dimensional and given b
 		\Pi_t = 
 		\begin{pmatrix}
 			f(D_{\text{KL}}, \lambda) & 1 - f(D_{\text{KL}}, \lambda) \\
-			0 & \sigma_{\text{move}}\\
+			1 -  q(\mObsVec_t^{\mRssiVec_\text{wifi}}) &  q(\mObsVec_t^{\mRssiVec_\text{wifi}})\\
 		\end{pmatrix}
 	\enspace ,
 	\label{equ:immpMatrix}