Application of System Identification Techniques for Integrated Navigation

Abstract

Land vehicle positioning relies mostly on Satellite navigation systems such as the Global Positioning System (GPS). However, GPS signals may be degraded or suffer from a blockage in urban canyons and tunnels, and the positioning information provided is interrupted. To obtain a continuous and reliable positioning solution, GPS is usually augmented with inertial sensors using Kalman Filter (KF). However, low-cost MEMS sensors suffer from complex error characteristics, which are difficult to model by the linearized KF models. System Identification Techniques can be employed to enhance the navigational solution. This paper reviews two algorithms to model and correct the residual and non-linear errors in challenging GPS environments using Parallel Cascade Identification (PCI), a non-linear system identification technique that is cascaded with the Kalman Filter (KF). PCI is first employed to model azimuth errors for a loosely coupled integration. The experimental results demonstrated that the KF performance was significantly improved by augmenting it with PCI to model the linear, non-linear, and other residual azimuth errors. Then PCI technique was employed for modeling residual pseudorange correlated errors to be used by a KF-based tightly coupled RISS/GPS navigational solution. PCI is successfully implemented to provide the non-linear model of pseudorange errors and augmented with tightly coupled KF to provide reliable and accurate positioning solution.