  In this paper, a new robust Student's t based stochastic cubature filter
(RSTSCF) is proposed for nonlinear state-space model with heavy-tailed process
and measurement noises. The heart of the RSTSCF is a stochastic Student's t
spherical radial cubature rule (SSTSRCR), which is derived based on the
third-degree unbiased spherical rule and the proposed third-degree unbiased
radial rule. The existing stochastic integration rule is a special case of the
proposed SSTSRCR when the degrees of freedom parameter tends to infinity. The
proposed filter is applied to a manoeuvring bearings-only tracking example,
where an agile target is tracked and the bearing is observed in clutter.
Simulation results show that the proposed RSTSCF can achieve higher estimation
accuracy than the existing Gaussian approximate filter, Gaussian sum filter,
Huber-based nonlinear Kalman filter, maximum correntropy criterion based Kalman
filter, and robust Student's t based nonlinear filters, and is computationally
much more efficient than the existing particle filter.
