1. A two-step robust adaptive filtering algorithm for GNSS kinematic precise point positioning
- Author
-
Long Zhao, Qieqie Zhang, and Luodi Zhao
- Subjects
0209 industrial biotechnology ,Computer science ,Mechanical Engineering ,Reliability (computer networking) ,Stability (learning theory) ,Adaptive filtering algorithm ,Aerospace Engineering ,02 engineering and technology ,Kinematics ,Precise Point Positioning ,01 natural sciences ,010305 fluids & plasmas ,Computer Science::Robotics ,020901 industrial engineering & automation ,GNSS applications ,Robustness (computer science) ,0103 physical sciences ,Key (cryptography) ,Algorithm - Abstract
In kinematic navigation and positioning, abnormal observations and kinematic model disturbances are one of the key factors affecting the stability and reliability of positioning performance. Generally, robust adaptive filtering algorithm is used to reduce the influence of them on positioning results. However, it is difficult to accurately identify and separate the influence of abnormal observations and kinematic model disturbances on positioning results, especially in the application of kinematic Precise Point Positioning (PPP). This has always been a key factor limiting the performance of conventional robust adaptive filtering algorithms. To address this problem, this paper proposes a two-step robust adaptive filtering algorithm, which includes two filtering steps: without considering the kinematic model information, the first step of filtering only detects the abnormal observations. Based on the filtering results of the first step, the second step makes further detection on the kinematic model disturbances and conducts adaptive processing. Theoretical analysis and experiment results indicate that the two-step robust adaptive filtering algorithm can further enhance the robustness of the filtering against the influence of abnormal observations and kinematic model disturbances on the positioning results. Ultimately, improvement of the stability and reliability of kinematic PPP is significant.
- Published
- 2021