Renamed moving average filter to box filter

tex/chapters/abstract.tex

@@ -7,7 +7,7 @@ However, in complex scenarios this frequently results in a poor representation,
 Recovering the probability density function using a kernel density estimation yields a promising approach to find the \qq{real} most probable state, but comes with high computational costs.
 Especially in time critical and time sequential scenarios, this turns out to be impractical.
 Therefore, this work uses techniques from digital signal processing in the context of estimation theory, to allow rapid computations of kernel density estimates. 
-The gains in computational efficiency are realized by substituting the Gaussian filter with an approximate filter based on the moving average filter.
+The gains in computational efficiency are realized by substituting the Gaussian filter with an approximate filter based on the box filter.
 Our approach outperforms other state of the art solutions, due to a fully linear complexity \landau{N} and a negligible overhead, even for small sample sets.
 Finally, our findings are tried and tested within a real world sensor fusion system.
 \end{abstract}