Abstract
A novel multi-sensor data fusion methodology is presented in this paper with respect to noise with unknown or randomly varying statistics properties and outliers in the SINS/GPS/Odometer integrated navigation system. The proposed methodology combines an adaptive interacting multiple model filtering (AIMM) and federated Kalman algorithm. The former implements dynamic interaction and dynamic change of multiple modes based on the Markov chain process of system models. To achieve the adaptive outlier detection and processing in the measurement signal, modified Kalman filter based on orthogonality of innovation serves as the parallel model filters in the AIMM approach. The advantage of decentralized filter architecture of the latter federated algorithm is flexibility and modularity. It has received considerable attention because of its outstanding fault detection and isolation capability. Experiment results show that the proposed multi-sensor data fusion methodology significantly improves the navigation estimation accuracy and reliability as compared to the federated extend Kalman filter and federated IMM filter approaches.
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Recommended by Associate Editor Chang Kyung Ryoo under the direction of Editor Hyun-Seok Yang. This journal was supported by the Natural Science Foundation of Anhui province (1708085QF146), Talent stabilization project of Anhui Science and Technology University (DQWD201601) and the Foundation of Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, China (SEU-MIAN-201701).
Lei Wang received the Ph.D. degree in control science and engineering from Southeast University, China, in 2015. He is currently a lecturer at Anhui Science and Technology University. His research interests include nonlinear filtering and estimation, sensor fusion, and statistical signal processing.
Shuangxi Li received his M.S. degree in electronics and communication engineering from Nanjing University of Posts and Telecommunications, China, in 2010. He is currently an associate professor at Anhui Science and Technology University. His research interests include nonlinear optimal control theory and nonlinear filtering.
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Wang, L., Li, S. Enhanced Multi-sensor Data Fusion Methodology based on Multiple Model Estimation for Integrated Navigation System. Int. J. Control Autom. Syst. 16, 295–305 (2018). https://doi.org/10.1007/s12555-016-0200-x
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DOI: https://doi.org/10.1007/s12555-016-0200-x