Quantitative comparison between Kalman filter and Particle filter for low cost INS/GPS integration

Recent technological advances in both GPS and low cost micro-electro mechanical system (MEMS)-based inertial sensors enabled monitoring the location of moving platforms for numerous positioning and navigation (POS/NAV) applications. When miniaturized inside any moving platforms, MEMS-based inertial navigation system (INS) can be integrated with GPS and enhance the performance in denied GPS environments (like in urban canyons). The combination of the two systems, traditionally performed by Kalman filtering (KF), exploits their complementary characteristics. Due to the inherent errors of MEMS inertial sensors and the relatively high noise levels associated with their measurements, KF has limited capabilities in providing accurate positioning. Particle filtering (PF) was recently suggested to accommodate for arbitrary inertial sensor characteristics, motion dynamics and noise distributions. This article gives detailed comparison between KF and PF as applied to MEMS-based INS/GPS integration and examines the performance of both methods during a road test experiment.