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In this paper, a new robust Student's t based stochastic cubature filter(RSTSCF) is proposed for nonlinear state-space model with heavy-tailed processand measurement noises. The heart of the RSTSCF is a stochastic Student's tspherical radial cubature rule (SSTSRCR), which is derived based on thethird-degree unbiased spherical rule and the proposed third-degree unbiasedradial rule. The existing stochastic integration rule is a special case of theproposed SSTSRCR when the degrees of freedom parameter tends to infinity. Theproposed 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 estimationaccuracy than the existing Gaussian approximate filter, Gaussian sum filter,Huber-based nonlinear Kalman filter, maximum correntropy criterion based Kalmanfilter, and robust Student's t based nonlinear filters, and is computationallymuch more efficient than the existing particle filter.

Robust Student's t based Stochastic Cubature Filter for Nonlinear Systems with Heavy-tailed Process and Measurement Noises