Heavy-tailed noise processing method based on cubature Kalman filtering under random loss of measurement
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.
nonlinear state estimationmeasurement random lossheavy-tailed noiseStudent's t distributionvariational Bayes