Name
Title | ||
|---|---|---|
Combined set-theoretic and stochastic estimation: A comparison of the SSI and the CS filter |
Abstract | ||
|---|---|---|
In estimation theory, mainly set-theoretic or stochastic uncertainty is considered. In some cases, especially when some statistics of a distribution are not known or additional stochastic information is used in a set-theoretic estimator, both types of uncertainty have to be considered. In this paper, two estimators that cope with combined stochastic and set-theoretic uncertainty are compared, namely the Set-theoretic and Statistical Information filter, which represents the uncertainty by means of random sets, and the Credal State filter, in which the state information is given by sets of probability density functions. The different uncertainty assessment in both estimators leads to different estimation results, even when the prior information and the measurement and system models are equal. This paper explains these differences and states directions, when which estimator should be applied to a given estimation problem. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIF.2010.5711908 | Information Fusion |
Keywords | Field | DocType |
estimation theory,filtering theory,set theory,state estimation,statistical analysis,credal state filter,estimation theory,set theory,statistical information filter,stochastic estimation,Filtering,random sets,sets of probability densities,state estimation | Applied mathematics,Random variable,Sensitivity analysis,Artificial intelligence,Estimation theory,Stochastic process,Measurement uncertainty,Uncertainty analysis,Statistics,Probability density function,Machine learning,Mathematics,Estimator | Conference |
ISBN | Citations | PageRank |
978-0-9824438-1-1 | 2 | 0.44 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vesa Klumpp | 1 | 103 | 11.23 |
| Benjamin Noack | 2 | 168 | 23.73 |
| Marcus Baum | 3 | 285 | 32.99 |
| Uwe D. Hanebeck | 4 | 599 | 71.02 |