Multi-sensor data fusion architecture based on adaptive Kalman filters and fuzzy logic performance assessment

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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. © 2002 Int. Soc. of Information Fusion.
Original languageAmerican English
Pages1542-1549
Number of pages1387
DOIs
StatePublished - 1 Jan 2002
Externally publishedYes
EventProceedings of the 5th International Conference on Information Fusion, FUSION 2002 -
Duration: 1 Jan 2002 → …

Conference

ConferenceProceedings of the 5th International Conference on Information Fusion, FUSION 2002
Period1/01/02 → …

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Escamilla-Ambrosio, P. J., & Mort, N. (2002). Multi-sensor data fusion architecture based on adaptive Kalman filters and fuzzy logic performance assessment. 1542-1549. Paper presented at Proceedings of the 5th International Conference on Information Fusion, FUSION 2002, . https://doi.org/10.1109/ICIF.2002.1021000