Tightly-coupled INS/GPS using Quaternion-based Unscented Kalman filter

PS receiver has dominated the field of positioning and navigation for decades [1]. However, its performance depends on the signal environments. It provides a continuous navigation solution only when more than four satellites are in view. In order to solve this problem, other navigation systems, e.g., inertial navigation system, are often employed to integrate with GPS for having a robust and continuous navigation solution. Historically, due to the high cost in manufacturing the inertial sensors, the INS/GPS integration system are mostly employed in military and aerospace industry [2, 3]. Recently, the advent of micro-electromechanical systems (MEMS) technology drives the discrete, heavy and inflexible inertial sensor system to small, cost-effective, light-weight, portable and lowpower silicon-based inertial devices. Although the cheap MEMS-based inertial sensors do not exhibit high accurate navigation performance, they can meet the requirements of many land-based navigation applications when aided with GPS devices. An INS/GPS system combines the advantages of both sides and provides accurate and uninterrupted navigation results, working in all environments, and constituting a potential and powerful alternative to the GPS alone navigation devices. Nowadays, the INS and GPS integrated solutions are the back-bones of many modern navigation systems, which are employed in industrial and military applications. Substantial research effort has been devoted to extensive algorithmic developments and performance analysis. The objective is mainly at the promotion of system estimation accuracy with low-cost sensor systems, putting a focus of interest onto powerful sensor fusion algorithms. The so-called tightly-coupled integration is one of the approaches to fuse the INS and GPS measurements. However, when modeling the underlying problem, the system propagation and observation models are nonlinear. The most common application of the Kalman filter (KF) on nonlinear systems is the extended (or linearized) Kalman filter (EKF) [4-6], which is based on a first-order linearization of the nonlinear stochastic system models with the assumption of Gaussian distributed noises. Although the EKF maintains the elegant and computationally efficient update form of the KF, it suffers from a number of drawbacks. That is, the linearized transformations are reliable, only if the error propagation can be well approximated by a linear function, because the small error