ENHANCING KALMAN FILTERING–BASED TIGHTLY COUPLED NAVIGATION SOLUTION THROUGH REMEDIAL ESTIMATES FOR PSEUDORANGE MEASUREMENTS USING PARALLEL CASCADE IDENTIFICATION

Abstract

Integrated global positioning system (GPS) solutions that utilize micro-electro-mechanical systems (MEMS)-based inertial sensors provide a more accurate navigation solution than stand-alone GPS in challenging scenarios. To keep the integrated solution less affected by sensor errors and to decrease the cost, a reduced inertial sensor system (RISS), which consists of only one gyroscope and two accelerometers, together with an odometer and integrated with GPS, is proposed. Tightly coupled integration is a better choice in demanding scenarios, as it can provide GPS aiding even when the number of visible satellites is three or less. However, inaccuracies of pseudoranges measured by the GPS receiver and used as aiding in the RISS/odometer/GPS integration solution will affect the overall positioning accuracy. This article explores the benefits of using parallel cascade identification (PCI), a nonlinear system identification technique that improves the overall navigation solution by modeling residual pseudorange correlated errors to be used by a Kalman filter (KF)–based tightly coupled RISS/odometer/GPS navigational solution. When less than four satellites are visible, the identified parallel cascade model for the still visible satellites is used to predict the residual pseudorange errors for these respective satellites, and the corrected pseudorange value is provided to KF. The performance of PCI for correcting the pseudoranges is examined and verified using road test trajectories and compared to a traditional tightly coupled RISS/odometer/GPS KF solution. The results demonstrate the advantages of this technique in correcting the pseudoranges and enhancing the positional solution.