Nonlinear stochastic dynamical systems are commonly used to model physical processes.
For linear and Gaussian systems, the Kalman filter is optimal in minimum mean squared …

Large-scale traffic systems require techniques that are able to 1) deal with high amounts of
data and heterogenous data coming from different types of sensors, 2) provide robustness in …

This paper introduces a novel method for positioning using coverage area estimates of
wireless communication nodes. The coverage areas are estimated in a Bayesian inference …

In this work, we propose an approximate minimum mean-square error filter for linear
dynamic systems with Gaussian Mixture (GM) noise. The proposed estimator tracks each …

The main problem for the state estimation with Gaussian mixture model is the exponentially
growing number of Gaussian components. To solve this problem, an efficient Gaussian sum …

The main task of a Trailing Suction Hopper Dredger (TSHD) is to excavate sediments from
the sea bottom while sailing and to transport them to a designated area. Its mobility and …

The estimation of weather parameters such as attenuation and rainfall rates from weather
radar data has been based mainly on deterministic regression models. The applications of a …

This note presents the box Gaussian mixture filter (BGMF), which is an efficient filter for the
systems with mainly linear measurements but enables utilizing highly nonlinear …

The term fingerprint-based (FP) positioning includes a wide variety of methods for
determining a receiver's position using a database of radio signal strength measurements …

The Kalman Filter (KF) is a classical algorithm that was developed for estimating a state that
evolves in time based on noisy measurements by assuming linear state transition and …