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toni
2018-02-15 14:51:08 +01:00
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tex/bare_conf.tex
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% not capitalized unless they are the first or last word of the title. % not capitalized unless they are the first or last word of the title.
% Linebreaks \\ can be used within to get better formatting as desired. % Linebreaks \\ can be used within to get better formatting as desired.
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\title{Fast Kernel Density Estimation using blah und blub} \title{Fast Kernel Density Estimation using Gaussian Filter Approximation}
% author names and affiliations % author names and affiliations
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tex/chapters/abstract.tex
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This is the abstract stract stract \begin{abstract}
It is common practice to use a sample-based representation to solve problems having a probabilistic interpretation.
In many real world scenarios one is then interested in finding a \qq{best estimate} of the underlying problem, e.g. the position of a robot.
This is often done by means of simple parametric point estimator, providing the sample statistics.
However, in complex scenarios this frequently results in a poor representation, due to a multimodal posteriors and a limited sample size.
linear complexity 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 turn 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.
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}
This will be shown in an a theoritical bases and also realistic ... blah