Sensor Fusion INS/GNSS based on Fuzzy Logic Adaptive Error-State Kalman Filter and Unscented Kalman Filter

Abstract

A Fuzzy Logic Adaptive Control (FLAC) is used to correct an Error-State Kalman Filter (ESKF) and an Unscented Kalman Filter (UKF), in a loosely coupled INS/GNSS system, when the IMU presents a dominant 1/f flicker noise. First, the ESKF and UKF implementation were tuned to achieve an optimal solution when all noise sources are white. Secondly, a flicker noise was applied to the IMU, making the systems reach a large error bound solution. Finally, a FLAC methodology that combines the observation of the residuals and the states error covariance and applies the correction using an exponential weighted and a process noise injection was used to correct the suboptimal behaviour. The FLAC application improved the navigation accuracy for all the states, preserving the stability of the error covariance. The comparison between the ESKF and UKF showed that both systems give equivalent outcomes, with the UKF been slightly less sensitive to disturbances.