Novel adaptive UKF for tightly-coupled INS/GPS integration with experimental validation on an UAV

Abstract

A Novel Adaptive Unscented Kalman Filter (NAUKF) has been developed and applied to fuse the outputs of strap-down IMU, the measurements of GPS satellites (pseudo-range and Doppler), strap-down magnetometer and a barometric altimeter, using tight coupling architecture. The proposed filter NAUKF considers the residual unmodeled noises of process and measurement as non-zero mean Gaussian white noises, estimates and compensates for the mean (bias) and covariance of the noise online, based on the principles of adaptive filtering, even if they are time-varying quantities. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained and the NAUKF is obtained. The noise statistic estimators designed in the proposed algorithm are built on the basis of forgetting factors which are usually calculated empirically. In this research a new calculation concept based on “Genetic Algorithm” as an optimization tool is utilized to determine the optimal values of forgetting factors. The performance and convergence of noise statistic estimators used in NAUKF are checked by Monte-Carlo simulation. The utilization of NAUKF for Tightly-Coupled INS/GPS Integration (TCI) has shown a superiority against the UKF for TCI especially during the GPS outages. The comparison is done experimentally by means of real flight trip of anUAV.