Development of an Adaptive Unscented Particle Filter for Tightly-coupled MEMS-INS/GPS Integrated Navigation System

With the recent advances in the computer technology, the particle filter (PF) turns out to be more and more attractive in navigation applications. However, its large computational burden still limits its widespread use. In this paper, the a posteriori estimates (i.e., mean and covariance) from the Adaptive Unscented Kalman filter (AUKF) are employed to specify the PF importance density function for generating particles. Unlike the sequential importance sampling re-sampling (SISR) particle filter, the re-sampling step is not required in the algorithm. Hence, the filter computational complexity can be reduced. Besides, the latest measurements are used to improve the proposal distribution for generating particles more intelligently. Simulations are conducted on the basis of a field collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption that a NovAtel DL-4plus GPS receiver and a Landmark(TM) 20 MEMS-based IMU are used. Navigations under benign and highly reflective signal environments are considered. Monte Carlo experiments are made. Numerical results show that the AUPF with 100 particles can present improved system estimation accuracy as compared with the AEKF and AUKF algorithms.