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- Title
量测随机丢失下基于容积卡尔曼滤波的厚尾噪声处理方法.
- Authors
李帅永; 聂嘉炜; 郭成春
- Abstract
A new nonlinear Kalman filtering method is proposed to address the problem of divergence in nonlinear state estimation under conditions of random measurement loss and heavy-tailed measurement noise. By introducing an auxiliary pa rameter that follows a Gamma distribution, the heavy-tailed measurement noise is modeled as a Student's t distribution to solve the problem of state estimation divergence caused by heavy-tailed noise. A random variable that follows a Benroulli distribution is used to describe the phenomenon of random measurement loss. Under conditions of random measurement loss, a joint posterior distribution is established based on the target state and unknown parameters, and a variational Bayesian method is used to jointly estimate the system state, measurement loss probability, and unknown heavy-tailed noise. Nonlinear target tracking simulation experiments show that the proposed algorithm can adaptively estimate the unknown meas urement loss probability. Under conditions of a 5% outlier probability, the root mean square error of the position, velocity, and rotation rate of the algorithm target tracking are 37%, 28%, and 60% respectively compared to the control algorithm. Under conditions of a 10% outlier probability, other algorithms have diverged, while the proposed algorithm can still track the target with low error, reflecting the good robustness and superiority of the proposed algorithm.
- Publication
Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition), 2024, Vol 36, Issue 3, p572
- ISSN
1673-825X
- Publication type
Article
- DOI
10.3979/j.issn.1673-825X.202302150035