The continuously growing population and urban growth rates are responsible for the sharp
rise in energy consumption, which leads to increased CO 2 emissions and demand-supply …

The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for
nonlinear systems. However, more than 35 years of experience in the estimation community …

Now in its second edition, this accessible text presents a unified Bayesian treatment of state-
of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state …

Due to the communication channel noises, GPS synchronization process, changing
environment temperature and different operating conditions of the system, the statistics of …

The unscented Kalman filter (UKF) has become a popular alternative to the extended
Kalman filter (EKF) during the last decade. UKF propagates the so called sigma points by …

In this paper, we are devoted to investigate the consensus-based distributed state estimation
problems for a class of sensor networks within the unscented Kalman filter (UKF) framework …

Probabilistic inference is the problem of estimating the hidden variables (states or
parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete …

In this paper, we propose a systematization of the (discrete-time) Unscented Kalman Filter
(UKF) theory. We gather all available UKF variants in the literature, present corrections to …

In this paper, a new version of the quadrature Kalman filter (QKF) is developed theoretically
and tested experimentally. We first derive the new QKF for nonlinear systems with additive …

We present a modular and extensible approach to integrate noisy measurements from
multiple heterogeneous sensors that yield either absolute or relative observations at …