Multiple model adaptive estimation algorithm for SINS/CNS integrated navigation system

• Published in: 2015 34th Chinese Control Conference (CCC) pp. 5286 - 5291
• Main Authors: Zhao, Fangfang, Zhao, Guangqiong, Fan, Shuangfei, Tang, Zhongliang, He, Wei
• Format: Conference Proceeding Journal Article
• Language: English
• Published: Technical Committee on Control Theory, Chinese Association of Automation 01.07.2015
• Subjects: Adaptation models; Adaptive algorithms; Adaptive systems; Algorithms; celestial navigation; Dynamical systems; Estimation; Filtering; Filtration; integrated navigation; Kalman filter; Kalman filters; Missiles; Multiple model adaptive estimation; Navigation; Navigation systems; Noise; Nonlinear dynamics; Sensors; strap-down inertial navigation
• ISSN: 1934-1768
• DOI: 10.1109/ChiCC.2015.7260464

In this paper, we investigate the Multiple Model Adaptive Estimation (MMAE) and present a new filtering method based on MMAE algorithm. This method is applied to the SINS/CNS integrated navigation system under the motion of ballistic missile. In this proposed algorithm, we use improved Kalman filters rather than traditional Kalman filters, such as Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF). And EKF and UKF are used as sub filters in MMAE algorithm to realize the state estimation of nonlinear system. Single model filters have poor adaptability, when system parameters are unknown or uncertainty. The proposed multiple model filters can solve this problem. As the simulation result shows, the improved filtering methods have better navigation accuracy, and can be more flexible when compared with traditional EKF and UKF algorithms, but pay for heavier computational burden.