PARITY VECTOR COMPENSATION FOR FAULT DIAGNOSIS OF INERTIAL SENSOR UNIT

摘要

A scheme based on neural network is presented to improve the fault detection and isolation (FDI) performance of a skewed redundant strapdown inertial measurement unit. The neural network is trained to eliminate the effects of input axis misalignment, scale factor errors and biases on parity vector. One advantage of the proposed technique over other compensation algorithm based on Kalman filter or bias separated estimation is that it does not require dynamic equation of error states and statistics of noise. The simulation results show that the proposed method can significantly improve the FDI performance of the skewed inertial sensor sets.