| Citation: | LI C H，MA J，YANG Y J，et al. Adaptively robust multi-sensor fusion algorithm based on square-root cubature Kalman filter[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（1）：220-228 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0201
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To deal with the problem that the square-root cubature Kalman filter (SRCKF) will have declined filtering performance or even filtering diverge when model errors and abnormal measurements occur, an adaptively robust multi-sensor fusion algorithm is proposed. Firstly, a robust subsystem is designed based on the innovation covariance matching principle to suppress abnormal measurements. Then, to overcome the model errors, an adaptive subsystem is designed based on the low complexity adaptive SRCKF (LCASRCKF) algorithm. Finally, according to the characteristics and limitations of the two subsystems, a global fusion architecture is proposed, which enables the system to fully balance and utilize the prior model prediction information and the posterior measurement information in the filtering process, and thus reduce the estimation error. The simulation results show that the proposed fusion algorithm has obvious advantages in terms of filtering accuracy, stability and convergence speed compared with the robust multiple fading factors cubature Kalman filter (RMCKF) and other algorithms.
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