Abstract
A fuzzy model-based multi-sensor data fusion system is presented in this paper. The system is capable of accommodating both non-linear sensors of the same type and different (non-commensurate) sensors and to give accurate information about the observed system state by combining readings from them at feature/decision level. The data fusion system consists of process model and knowledge-based sensor model units based on a fuzzy inference system that predicts the future system and sensor states based on the previous states and the inputs. The predicted state is used as a reference datum in the sensor validation process which is conducted through a fuzzy classifier to categorise each sensor reading as a valid or invalid datum. The data fusion unit combines the valid sensor data to generate the feature/decision output. The corrector unit functions as a filtering unit to provide the final decision on the value of the current state based on the current measurement (fused output) and the predicted state. The results of the simulation of this system and other data fusion systems have been compared to justify the capability of the system.
| Original language | English |
|---|---|
| Pages (from-to) | 301-312 |
| Number of pages | 12 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 4385 |
| DOIs | |
| Publication status | Published - 2001 |
| Event | Sensor Fusion: Architectures, Algorithms and Applications V - Orlando, FL, United States Duration: 18 Apr 2001 → 20 Apr 2001 |
Keywords
- Function approximation
- Fuzzy inference system
- Multisensor data fusion
- Predictive-based data fusion method