Abstract
We consider systems whose parameters satisfy certain easily computable physical laws. Each parameter is directly measured by a number of sensors, or estimated using measurements, or both. The measurement process may introduce both systematic and random errors which may then propagate into the estimates. Furthermore, the actual parameter values are not known since every parameter is measured or estimated, which makes the existing sample-based fusion methods inapplicable. We propose a fusion method for combining the measurements and estimators based on the least violation of physical laws that relate the parameters. Under fairly general smoothness and nonsmoothness conditions on the physical laws, we show the asymptotic convergence of our method and also derive distribution-free performance bounds based on finite samples. For suitable choices of the fuser classes, we show that for each parameter the fused estimate is probabilistically at least as good as its best measurement as well as best estimate. We illustrate the effectiveness of this method for a practical problem of fusing well-log data in methane hydrate exploration.
Original language | English |
---|---|
Pages (from-to) | 66-77 |
Number of pages | 12 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 27 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2005 |
Funding
The authors gratefully acknowledge the extensive and constructive comments from generous anonymous reviewers that greatly improved the presentation of this paper. This research is sponsored by the Materials Sciences and Engineering Division, Office of Basic Energy Sciences, US Department of Energy, under Contract No. DE-AC05-00OR22725 with UT-Battelle, LLC, and the US Office of Naval Research under order N00014-96-F-0415.
Keywords
- Covering numbers
- Distribution free bounds
- Information fusion
- Methane hydrates exploration
- Physical laws
- Sensor fusion
- Vapnik-Chervonenkis theory