High-precision relative positioning and navigation is a fundamental requirement for many applications such as flight formation, spacecraft docking and collision avoidance. The main purpose of this paper is to develop a robust multi-model estimation algorithm for reliable navigation when there are abnormities of measurement and motion. In order to deal with these abnormities, we propose a quantitative evaluation method of relative navigation system by introducing the degree of observability (DoO) and the degree of abnormity (DoA). In addition, we design a feedforward information fusion and a feedback information allocation method based on DoO and DoA, and thus form a multi-model robust estimation algorithm. In order to testify the effectiveness and robustness of the proposed algorithm, a practical experiment with real data sets gathered in urban areas has been carried out. The results showed that the maximum relative positioning RMSE reduction ratio can reach 75%, and the maximum relative velocity RMSE reduction ratio can reach 51% compared with EKF. Therefore, the proposed method can guarantee the accuracy and robustness of relative navigation under abnormal conditions.
Robust Multi-Model Estimation for Reliable Relative Navigation Based on Observability and Abnormity Analysis
IEEE Transactions on Intelligent Transportation Systems ; 24 , 5 ; 5144-5158
2023-05-01
2802696 byte
Article (Journal)
Electronic Resource
English
Multi-Sensors Observability Analysis on Pedestrian Navigation System
British Library Conference Proceedings | 2011
|