A particle filtering (PF) is a sequential Bayesian filtering method suitable for non-linear non-
Gaussian systems, which is widely used to estimate the states of parameters of interest that …

For non-ellipsoidal extended object and group target tracking (NEOT and NGTT), using a
random matrix to simplify the extension as an ellipsoid, although efficient, may not be …

This paper presents a novel particle filter based dynamic state estimation scheme for power
systems where the states of all the generators are estimated. The proposed estimation …

Accurate real-time state estimation plays an important role in distributed generation (DG)
networks. In order to establish a reliable communication link for estimation, an information …

The development of distributed generation raises high requirement on accurate real-time
state estimation with phasor measurement unit (PMU) for wide-area measurement systems …

Leveraging multiple heterogeneous measurements to predict wind power has long been a
challenging task in the electrical community. In this paper, a deep architecture incorporated …

Objectives: We propose a parallel hybrid particle swarm optimization (PHPSO) algorithm to
reduce the computation cost because solving the one-dimensional (1D) heat conduction …

In this paper, the filtering problem for systems with fading measurements is considered.
Taking advantage of the cascaded structure of the system, the original filtering problem is …

In this paper, we present the implementation of two types of Bayesian inference problems to
demonstrate the potential of building probabilistic algorithms in hardware using single set of …

Particle filtering provides an approximate representation of a tracked posterior density which
converges asymptotically to the true posterior as the number of particles used increases …