A new nonlinear Kalman filtering method is proposed to address the problem of divergence in nonlinear state esti-mation 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 Bayes-ian method is used to jointly estimate the system state,measurement loss probability,and unknown heavy-tailed noise.Non-linear 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.
关键词
非线性状态估计/量测随机丢失/厚尾噪声/Student'st分布/变分贝叶斯
Key words
nonlinear state estimation/measurement random loss/heavy-tailed noise/Student's t distribution/variational Bayes
维普
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