From e08b30239a8374dcb01af9f3240056d6230871fd Mon Sep 17 00:00:00 2001 From: toni Date: Tue, 6 Feb 2018 19:25:02 +0100 Subject: [PATCH] working on introduction --- tex/chapters/introduction.tex | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/tex/chapters/introduction.tex b/tex/chapters/introduction.tex index 8c64486..9440699 100644 --- a/tex/chapters/introduction.tex +++ b/tex/chapters/introduction.tex @@ -3,8 +3,14 @@ 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