| Citation: | LI Chunhui, MA Jian, YANG Yongjian, et al. Low-complexity adaptive cubature Kalman filter algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(4): 716-724. doi: 10.13700/j.bh.1001-5965.2020.0642(in Chinese)
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Cubature Kalman filter (CKF) with good filtering performance is one of the deterministic sampling filtering algorithms, but it is not able to overcome the impact caused by the target model uncertainty or the mutation of the target state. Constructing strong tracking CKF can effectively improve the adaptability of the algorithm, but the computation is greatly increased when solving the fading factor. A low-complexity adaptive CKF algorithm is proposed to solve the above problems. By establishing adaptive judgment criteria and amending method based on innovation sequence, the predicted state value is directly amended, so that the filtering algorithm can keep up with the real state of the target in time, and thus improve the filtering accuracy. The complexity of CKF, strong tracking CKF and the proposed algorithm are calculated and analyzed by using floating point operations. At the same time, the above three algorithms are applied to target tracking with inaccurate modeling, and are verified through simulation. The simulation results show that both the proposed algorithm and the strong tracking CKF algorithm can maintain better filtering accuracy and numerical stability in the case of mismatched target modeling, and the proposed algorithm has obvious improvement in algorithm complexity.
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