A Hybrid Kalman Filter-fuzzy Logic Multisensor Data Fusion Architecture with Fault Tolerant Characteristics

In this work a novel Multi-Sensor Data Fusion (MSDF) architecture with fault tolerant characteristics is proposed. This MSDF architecture is based on Kalman filtering and fuzzy logic techniques. First, the measurement coming from each sensor is fed to a fuzzy–adapted Kalman filter (FKF). The adaptation is in the sense of adjusting the measurement noise covariance matrix R using a fuzzy inference system (FIS) based on a covariance matching technique. Second, another FIS, here called a fuzzy logic observer (FLO), is used to monitor the performance of each FKF. The FLO assigns a degree of confidence to each one of the FKFs. The degree of confidence indicates to what level each FKF output reflects the true value of the parameter being measured. At this level transient sensor faults are eliminated. Finally, the fused estimated measurement is obtained through a defuzzification process based on these confidence values. At this level persistent sensor faults are eliminated using a voting scheme. To demonstrate the effectiveness and accuracy of this hybrid MSDF architecture, an example with four noisy and faulty sensors is outlined. The results show very good performance.