In this letter, we analyze certain student's t-filters for linear Gaussian systems with
misspecified noise covariances. It is shown that under appropriate conditions, the filter both …

Kernel quadratures and other kernel-based approximation methods typically suffer from
prohibitive cubic time and quadratic space complexity in the number of function evaluations …

The performance of the state estimation for Gaussian state space models can be degraded if
the models are affected by the non-Gaussian process and measurement noises with …

Bayesian cubature provides a flexible framework for numerical integration, in which a priori
knowledge on the integrand can be encoded and exploited. This additional flexibility …

In this article, we propose a robust diffusion nonlinear filtering algorithm based on Student-t
distribution for distributed sensor networks (DSNs). Existing distributed state estimation …

The sigma-point filters, such as the unscented Kalman filter, are popular alternatives to the
ubiquitous extended Kalman filter. The classical quadrature rules used in the sigma-point …

The cubature Kalman filter (CKF) has poor performance in strongly nonlinear systems while
the cubature particle filter has high computational complexity induced by stochastic …

Many models require integrals of high-dimensional functions: for instance, to obtain
marginal likelihoods. Such integrals may be intractable, or too expensive to compute …

This chapter discusses the elements of a Dynamic Data Driven Applications System in the
context of mapping coherent environmental fluids using autonomous small unmanned …

This paper considers state estimation of stochastic systems with outliers in measurements.
Traditional filters, which assume Gaussian-distributed measurement noise, may have …