Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration

Abstract

The INS/GNSS integration is the commonly used technique for hypersonic vehicle navigation. However, owing to the complicated flight dynamics with high maneuverability and large flight envelope, the dynamic model of INS/GNSS integration inevitably exists errors which degrades the navigation performance of a hypersonic vehicle seriously. In this paper, a new model predictive based unscented Kalman filter (MP-UKF) is proposed to address this problem. The MP-UKF employs the concept of model predictive filter for the establishment of a dynamic model error estimator, and it subsequently compensate the model error estimation to UKF for nonlinear state estimation. Since the MP-UKF could predict the dynamic model error persistently and correct the filtering procedure of UKF online, it improves the UKF adaptiveness and is promising for the performance enhancement of INS/GNSS integration for hypersonic vehicle navigation. Simulation results and comparison analysis have been conducted to demonstrate the effectiveness of the proposed method.