Resumen
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.
Idioma original | Inglés |
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Páginas | 1542-1549 |
Número de páginas | 8 |
DOI | |
Estado | Publicada - 2002 |
Publicado de forma externa | Sí |
Evento | 5th International Conference on Information Fusion, FUSION 2002 - Annapolis, MD, Estados Unidos Duración: 8 jul. 2002 → 11 jul. 2002 |
Conferencia
Conferencia | 5th International Conference on Information Fusion, FUSION 2002 |
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País/Territorio | Estados Unidos |
Ciudad | Annapolis, MD |
Período | 8/07/02 → 11/07/02 |