15 lines
1.4 KiB
TeX
15 lines
1.4 KiB
TeX
\begin{abstract}
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It is common practice to use a sample-based representation to solve problems having a probabilistic interpretation.
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In many real world scenarios one is then interested in finding a \qq{best estimate} of the underlying problem, \eg{} the position of a robot.
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This is often done by means of simple parametric point estimators, providing the sample statistics.
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However, in complex scenarios this frequently results in a poor representation, due to multimodal densities and limited sample sizes.
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Recovering the probability density function using a kernel density estimation yields a promising approach to solve the state estimation problem \ie{} finding the \qq{real} most probable state, but comes with high computational costs.
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Especially in time critical and time sequential scenarios, this turns out to be impractical.
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Therefore, this work uses techniques from digital signal processing in the context of estimation theory, to allow rapid computations of kernel density estimates.
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The gains in computational efficiency are realized by substituting the Gaussian filter with an approximate filter based on the box filter.
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Our approach outperforms other state of the art solutions, due to a fully linear complexity and a negligible overhead, even for small sample sets.
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Finally, our findings are evaluated and tested within a real world sensor fusion system.
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\end{abstract}
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