UKF-based fault detection and isolation algorithm for IMU sensors of Unmanned Underwater Vehicles

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned underwater vehicles (UUV) development, since control system reliability strongly depends on the accuracy of attitude estimation. In small UUVs, typical redundant architectures, based on triplex redundancy, can represent a strong limitation in terms of payload and power consumption. This work proposes an FDI algorithm for small UUVs equipped with duplex sensor architecture. Here, attitude estimation relies upon the usage of two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The proposed SFDI algorithm is based on an unscented Kalman filter (UKF) approach to efficiently detect and isolate faulted IMU sensors. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real underwater vehicle. A sensitivity analysis was carried out on the relevant algorithm parameters in order to find a trade-off between performance, computational burden and reliability.