Abstract
In this study, an innovation approach based new sensor Fault Detection and Isolation (FDI) method, which is sensitive to the changes in the innovation mean and covariance of the Kalman filter is proposed. The multiple measurement noise scale factors (MMNSFs) are used in this method as the monitoring statistics. Multiple measurement noise scale factors are determined in order to make it possible to perform the sensor fault detection and isolation operations simultaneously. Proposed innovation approach based sensor FDI algorithm with MMNSF is applied to the dynamic model of an Unmanned Aerial Vehicle (UAV) platform. The single sensor faults are considered. The proposed FDI algorithms are tested for the two different measurement malfunction scenarios; continuous bias at measurements, measurement noise increment.
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