Abstract
In this work a novel multi-sensor data fusion (MSDF) architecture is presented. First, each measurement-vector coming from each sensor is fed to a fuzzy logic-based adaptive Kalman filter (FL-AKF); thus there are N sensors and N FL-AKFs working in parallel. The adaptation in each FL-AKF is, in the sense of dynamically tuning the measurement noise covariance matrix R, employing a fuzzy inference system (FIS) based on a covariance matching technique. A second FIS, called a fuzzy logic assessor (FLA), monitors and assesses the performance of each FL-AKF. The FLA assigns a degree of confidence, a number on the interval [0, 1], to each of the FL-AKF outputs. Finally, a defuzzification scheme obtains the fused state-vector estimate based on confidence values. The effectiveness and accuracy of this approach is demonstrated using a simulated example. Two defuzzification methods are explored and compared, and results show good performance of the proposed approach.
Original language | English |
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Pages | 1542-1549 |
Number of pages | 8 |
DOIs | |
State | Published - 2002 |
Externally published | Yes |
Event | 5th International Conference on Information Fusion, FUSION 2002 - Annapolis, MD, United States Duration: 8 Jul 2002 → 11 Jul 2002 |
Conference
Conference | 5th International Conference on Information Fusion, FUSION 2002 |
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Country/Territory | United States |
City | Annapolis, MD |
Period | 8/07/02 → 11/07/02 |
Keywords
- Adaptive Kalman filtering
- Fuzzy logic
- Multisensor data fusion
- Performance assessment