Improvements in terrain-based road vehicle localization by initializing an Unscented Kalman Filter using Particle Filters

This work develops an algorithm to initialize an Unscented Kalman Filter using a Particle Filter for applications with initial non-Gaussian probability density functions. The method is applied to estimating the position of a road vehicle along a one-mile test track and 7 kilometer span of a highway using terrain-based localization where the pitch response of the vehicle is compared to a pre-measured pitch map of each roadway. The results indicate that the method can be used to decrease the computational load of the algorithm while maintaining the accuracy of the Particle Filter, but that the challenge is to determine the appropriate moment to perform the switch between algorithms. A modified Chi-Squared test is used to determine a switchover point when the probability density function of the particle population can be approximated by a Gaussian for initializing the Unscented Kalman Filter. A normalized innovation squared test is also demonstrated to be useful for monitoring the health of the Unscented Kalman Filter.