Strong tracking CKF adaptive interactive multiple model tracking algorithm based on hypersonic target

被引：1
|
作者：
Luo, Yalun [1 ]
Liao, Yurong [2 ]
Li, Zhaoming [2 ]
Ni, Shuyan [2 ]
机构：
[1] Department of Graduate School, Space Engineering University, Beijing,101416, China
[2] Department of Electronics and Optics, Space Engineering University, Beijing,101416, China
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2024年 / 50卷 / 07期
关键词：
Hypersonic targets have complex motion states and high maneuverability. The conventional interactive multiple model (IMM) technique converges slowly and tracks poorly. Based on numerous fading variables; an adaptive interactive multiple model (AIMM) algorithm with strong tracking for cubature Kalman filter (CKF) is proposed. The structure of CKF is examined based on IMM-CKF; and the fading factor of the strong tracking algorithm is added to the covariance matrix of time updating and measurement updating. This allows for the online and real-time adjustment of the filter gain; which can lessen the decrease in filter accuracy brought on by model mismatch. Choose the Singer; current’ and Jerk models from the IMM model collection. These models introduce singular value decomposition (SVD) decomposition as a solution to the issue that the model dimension expansion prevents Cholesky decomposition in CKF. An adaptive algorithm for Markov matrix in IMM algorithm is proposed. The transition probability is adaptively modified by the value of the model likelihood function to enhance the proportion of the matching model. Simulation results show that the proposed algorithm improves tracking convergence speed by 37.5% and tracking accuracy by 16.51%. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved;
D O I：
10.13700/j.bh.1001-5965.2022.0587
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：2272 / 2283
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