Adaptive extended Kalman filter for a red shift navigation system

In this paper, a navigation system based on red shift for spacecraft mission in solar system is implemented. Deep space environment is complex and changeable, its characteristics are hard to handle, there exist difficulties to simulate the deep space environment accurately. We know that red shift navigation system can achieve high navigation accuracy in deep space missions. And innovation-based adaptive estimation(IAE) can utilize latest measurement to adaptive adjust the covariance matrix of observation equations and state error covariance matrix. Using IAE method, we proposed a new adaptive filter to handle variable deep space environment. Employing adaptive extended Kalman filter (AEKF) in this system, the navigation performance is analysed. Simulation results indicate that the proposed navigation system using AEKF can achieve higher precision, better availability and continuity compared with conventional extended Kalman filter (EKF), but greater computational burden.