Aiming at the nonlinear state estimation problem with heavy-tailed process measurement noise and randomly delayed measurements

this paper deduces a new robust student's t filter framework by taking into one-step random delay characteristics and heavy-tailed characteristics of process noise and measurement noise account fully. Meanwhile

this paper proposes a new robust Student's t-based stochastic cubature filter(RSTCF)by approximately calculating the Student's t weight integral using stochastic Student's t-spherical radial cubature rule. Firstly

this paper uses a set of stochastic sequences obeying the Bernoulli distribution to describe the possible one-step random delay phenomenon in the system

what's more

this paper also uses the student's t distribution to characterize the heavy-tailed characteristics of the process noise and measurement noise. Secondly

it's theoretically proved that the robust Student's t filter automatically will be reduced to a standard nonlinear Gaussian approximation filter when the degrees of freedom in the posterior probability density function of the state and measurement noise are increasing continuously and randomly delay probability is equal to zero. Finally

this paper presentes a new robust Student's t-based stochastic cubature filter by use of stochastic Student's t-spherical radial cubature rule. The effectiveness and superiority of the filter are verified by the coordinated turn maneuvers model.