INITIAL LEVELING OF STRAPDOWN INERTIAL NAVIGATION SYSTEM WITH AN ONLINE ROBUST INPUT ESTIMATOR

Initial leveling of strapdown inertial navigation system is a prerequi site work for distinguishing between gravity and acceleration effects in the accelerometer sensing's. This study presents an on-line methodo logy to resolve the initial leveling problem of a vehicle, which is su bject to a large, long duration, and abrupt disturbance input with a d eterministic nature under noisy circumstances. The developed method he rein is the Karman filter based scheme with a robust input estimator, generalized M estimator, and a testing criterion. The generalized M es timator identifies the unexpected disturbance inputs in real time. In addition, hypothetical testing based on the least-squares estimator is devised to detect the input's onset and presence. A required regressi on equation between the observed value of the residual sequence with a n unknown input and theoretical residual sequence of the Kalman filter with no input is formulated. Input estimation and detection are then provided on the basis of the derived regression equation. Moreover, Mo nte Carlo simulations are performed to assess the superior capabilitie s of the proposed method in term of rapid responses, accuracy, and rob ustness. The efficient initial leveling can facilitate the entire alig nment of the inertial system.