Performance Analysis of an Adaptive Kalman Filter for GNSS/INS Deeply Coupled Navigation System

Abstract

The deeply coupled GNSS/INS navigation system has drawn wide attention due to its dynamic adaptability and immunity to the jamming. This paper proposes an adaptive Kalman filer (AKF) design approach for the deeply integration. The AKF is used to gain the optimal estimation of states under high dynamics. Experimental results show that the standard deviations of AKF reduced by 3 times than traditional KF method, and the proposed AKF algorithm performs better than the KF method and has better dynamic adaptability.