An adaptive unscented kalman filter for tightly-coupled INS/GPS integration

Date

2012

Author

Akça, Tamer

Metadata

Show full item record

Item Usage Stats

18
views

17
downloads

Cite This

In order to overcome the various disadvantages of standalone INS and GPS, these systems are integrated using nonlinear estimation techniques and benefits of the two complementary systems are obtained at the same time. The standard and most widely used estimation algorithm in the INS/GPS integrated systems is Extended Kalman Filter (EKF). Linearization step involved in the EKF algorithm can lead to second order errors in the mean and covariance of the state estimate. Another nonlinear estimator, Unscented Kalman Filter (UKF) approaches this problem by carefully selecting deterministic sigma points from the Gaussian distribution and propagating these points through the nonlinear function itself leading third order errors for any nonlinearity. Scaled Unscented Transformation (SUT) is one of the sigma point selection methods which gives the opportunity to adjust the spread of sigma points and control the higher order errors by some design parameters. Determination of these parameters is problem specific. In this thesis, effects of the SUT parameters on integrated navigation solution are investigated and an “Adaptive UKF” is designed for a tightly-coupled INS/GPS integrated system. Besides adapting process and v measurement noises, SUT parameters are adaptively tuned. A realistic fighter flight trajectory is used to simulate IMU and GPS data within Monte Carlo analysis. Results of the proposed method are compared with standard EKF and UKF integration. It is observed that the adaptive scheme used in the sigma point selection improves the performance of the integrated navigation system especially at the end of GPS outage periods.

Subject Keywords

Kalman filtering., Inertial navigation systems., Global Positioning System.

URI

http://etd.lib.metu.edu.tr/upload/12614049/index.pdf
https://hdl.handle.net/11511/21337

Collections

Graduate School of Natural and Applied Sciences, Thesis