Two approaches for on-line Gaussian process regression with low computational and
memory demands are proposed. The first approach assumes known hyperparameters and …

Target tracking algorithms usually assume that the received measurements stem from a
point source. However, in many scenarios this assumption is not feasible so that …

Oral diseases are very prevalent worldwide and affect people of all ages. Dentists depend
on x-rays to study the characteristics of oral diseases. The segmentation and analysis of …

The implementation challenges of cooperative localization by dual foot-mounted inertial
sensors and inter-agent ranging are discussed, and work on the subject is reviewed. System …

The Kalman filter and its extensions are used in a vast number of aerospace and navigation
applications for nonlinear state estimation of time series. In the literature, different …

In this research, we propose a method with three search algorithms, namely Greedy,
Dynamic, and Hybrid, to exploit the ability of a drone to efficiently and accurately locate air …

An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems called Smart
Sampling Kalman Filter (S 2 KF) is introduced. It is based on a new low-discrepancy Dirac …

In this paper, we introduce a new sample-based Gaussian filter. In contrast to the popular
Nonlinear Kalman Filters, eg, the UKF, we do not rely on linearizing the measurement …

We consider estimating the hidden state of a discretetime stochastic nonlinear dynamic
system based on noisy measurements through Bayesian inference. This is an important …

A new Gaussian filter for estimating the state of nonlinear systems is derived that relies on
two main ingredients: i) the progressive inclusion of the measurement information and ii) a …