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working on introduction

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toni
2018-02-06 19:25:02 +01:00
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tex/chapters/introduction.tex
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Sensor fusion approaches are often based upon probabilistic descriptions like particle filters, using samples to represent the distribution of a dynamical system.
To update the system recursively in time, probabilistic sensor models process the noise measurements and a state transition function provides the system's dynamics.
Therefore a sample or particle is a representation of one possible system state, e.g. the position of a pedestrian within a building.
In most real world scenarios one is then interested in finding the most probable state within the state space.
In the discrete manner of the sample representation this is often done by
In most real world scenarios one is then interested in finding the most probable state within the state space, to provide the "best estimate" of the underlying problem.
In the discrete manner of a sample representation this is often done by calculating a single value, also known as sample statistic, to serve as a "best guess".
This values is often calculated by means of simple parametric point estimators, e.g. using weighted-average of all samples or that one sample with the highest overall weight \cite{}.
%da muss es doch noch andere methoden geben... verflixt und zugenäht... aber grundsätzlich ist ein weighted average doch ein point estimator? (https://www.statlect.com/fundamentals-of-statistics/point-estimation)
%multimodalities...
%interested in the most proper state within the state space of the dynamic system
%echte antwort computationel complex deswegen %weighted-average -> problem multimodal; sample mit höhsten wert -> springt viel rum