PERFORMANCE OF GNSS CARRIER-TRACKING LOOP BASED ON KALMAN FILTER IN A CHALLENGING ENVIRONMENT
- 1Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, China
- 2Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute of Technology), Ministry of Education, Beijing, China
- 3Department of Geomatics Engineering, University of Calgary, Calgary, Canada
Keywords: Global Navigation Satellite System (GNSS), Carrier Tracking Loop, Extended Kalman Filter (EKF), High Dynamics, Challenging Environment, Innovation sequence
Abstract. The global navigation satellite system (GNSS) recently plays an extremely important role in positioning, navigation, and timing (PNT) applications for the modernized automations and mechanizations, e.g., unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), military aircrafts, etc. Nevertheless, GNSS signals are very vulnerable to the influence of various interferences when they are received on Earth, and the reason why it happens is that the long line-of-sight (LOS) distance between the satellite and the receiver user dramatically reduces the power strength after the signal reaches at the ground. The weak GNSS signal is hard to be handled with traditional phase lock loop (PLL), especially in a dynamic environment. Again, the trade-off among the coherent integration time of tracking loop, received signal power strength, and signal or user receiver dynamics is still a tough and remained problem to be solved. The Kalman filter (KF) is always a promising tool to efficiently decrease the random noise for the tracking process. In our work, we evaluate the performances of the tracking loop modelled with both standard KF and extended Kalman filter (EKF). An adaptive algorithm for the covariance matrix of the process noise is contained in our system to increase the tracking ability in a weak and dynamic environment. Besides, a noise channel is also contained to automatically adjust the priori measurement covariance for the KF tracking loop model. Simulation results demonstrate the performance with the proposed technique.