An Adaptive Unscented Kalman Filter for tightly coupled INS/GPS integration

In order to overcome the various disadvantages of standalone INS and GPS, these systems are integrated using nonlinear estimation techniques. The standard and most widely used estimation algorithm for the INS/GPS integration is Extended Kalman Filter (EKF) which makes a first order approximation for the nonlinearity involved. Unscented Kalman Filter (UKF) approaches this problem by carefully selecting deterministic sigma points from Gaussian distributions and propagating these points through the nonlinear function itself. Scaled Unscented Transformation (SUT) is one of the sigma point selection methods which give the opportunity to adjust the spread of sigma points and control the higher order errors by some design parameters. Determination of these design parameters is problem specific. In this paper, an adaptive approach in selecting SUT parameters is proposed for tightly-coupled INS/GPS integration. Results of the proposed method are compared with the EKF and UKF integration. It is observed that the Adaptive UKF has slightly improved the performance of the navigation system especially at the end of GPS outage periods.