The Kalman filter (KF) has received a huge interest from the industrial electronics community
and has played a key role in many engineering fields since the 1970s, ranging, without …

Since the landmark work of RE Kalman in the 1960s, considerable efforts have been
devoted to time series state space models for a large variety of dynamic estimation …

In this paper, a novel nonlinear filter named Sparse-grid Quadrature Filter (SGQF) is
proposed. The filter utilizes weighted sparse-grid quadrature points to approximate the multi …

Herein is presented an efficient nonlinear filtering algorithm called the Gaussian-sum
cubature Kalman filter (GSCKF) for the bearings-only tracking problem. It is developed …

Target state estimation is a key technology for unmanned underwater vehicles (UUVs) to
achieve target tracking, collision avoiding, formation control, and other tasks. Compared with …

This paper develops a probabilistic anticipation algorithm for dynamic objects observed by
an autonomous robot in an urban environment. Predictive Gaussian mixture models are …

Experimental design attempts to maximise the information available for modelling tasks. An
optimal experiment allows the inferred models or parameters to be chosen with the highest …

Many problems in navigation and tracking require increasingly accurate characterizations of
the evolution of uncertainty in nonlinear systems. Nonlinear uncertainty propagation …

Using neural networks to address data-driven problems often entails dealing with
uncertainties. However, the propagation of uncertainty through a network's nonlinear layers …

Monostatic and bistatic position and Doppler measurements used in radar and sonar
systems are nonlinear transformations of a Cartesian state. These nonlinearities pose a …