Multisensor Data Fusion Architecture Based on Adaptive Kalman Filters and Fuzzy Logic Performance Assessment

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. Second, another FIS, here called a fuzzy logic assessor (FLA), is monitoring and assessing the performance of each FLAKF. The FLA assigns a degree of confidence, a number on the interval [0, 1], to each one of the FL-AKF outputs. Finally, a defuzzification scheme obtains the fused statevector estimate based on the confidence values. The effectiveness and accuracy of this approach is demonstrated in a simulated example. Two defuzzification methods are explored and compared; results show good performance of the proposed approach.