An overview of existing methods and recent advances in sequential Monte Carlo
It is now over a decade since the pioneering contribution of Gordon (1993), which is
commonly regarded as the first instance of modern sequential Monte Carlo (SMC) …
commonly regarded as the first instance of modern sequential Monte Carlo (SMC) …
Localization prediction in vehicular ad hoc networks
Localization systems play a major role in many applications for vehicular ad hoc networks
(VANETs). One of the most interesting problems to be solved in vehicular networks is how to …
(VANETs). One of the most interesting problems to be solved in vehicular networks is how to …
Particle filter theory and practice with positioning applications
F Gustafsson - IEEE Aerospace and Electronic Systems …, 2010 - ieeexplore.ieee.org
The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear
Bayesian filtering problem, and there is today a rather mature theory as well as a number of …
Bayesian filtering problem, and there is today a rather mature theory as well as a number of …
Particle filtering
PM Djuric, JH Kotecha, J Zhang… - IEEE signal …, 2003 - ieeexplore.ieee.org
Recent developments have demonstrated that particle filtering is an emerging and powerful
methodology, using Monte Carlo methods, for sequential signal processing with a wide …
methodology, using Monte Carlo methods, for sequential signal processing with a wide …
In-car positioning and navigation technologies—A survey
In-car positioning and navigation has been a killer application for Global Positioning System
(GPS) receivers, and a variety of electronics for consumers and professionals have been …
(GPS) receivers, and a variety of electronics for consumers and professionals have been …
Marginalized particle filters for mixed linear/nonlinear state-space models
The particle filter offers a general numerical tool to approximate the posterior density
function for the state in nonlinear and non-Gaussian filtering problems. While the particle …
function for the state in nonlinear and non-Gaussian filtering problems. While the particle …
Stack autoencoder transfer learning algorithm for bearing fault diagnosis based on class separation and domain fusion
M Sun, H Wang, P Liu, S Huang… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
In intelligent fault diagnosis, transfer learning can reduce the requirement of sufficient
labeled data and the same data distribution. However, for the diagnosis of a new machine …
labeled data and the same data distribution. However, for the diagnosis of a new machine …
Particle filter and Levy flight-based decomposed multi-objective evolution hybridized particle swarm for flexible job shop greening scheduling with crane transportation
B Zhou, X Liao - Applied Soft Computing, 2020 - Elsevier
Since greening scheduling is arousing increasing attention from many manufacturing
enterprises, this paper focuses on a flexible job shop greening scheduling problem with …
enterprises, this paper focuses on a flexible job shop greening scheduling problem with …
Particle filter for fault diagnosis and robust navigation of underwater robot
A particle filter (PF)-based robust navigation with fault diagnosis (FD) is designed for an
underwater robot, where 10 failure modes of sensors and thrusters are considered. The …
underwater robot, where 10 failure modes of sensors and thrusters are considered. The …
An improvement on resampling algorithm of particle filters
X Fu, Y Jia - IEEE Transactions on Signal Processing, 2010 - ieeexplore.ieee.org
In this correspondence, an improvement on resampling algorithm (also called the systematic
resampling algorithm) of particle filters is presented. First, the resampling algorithm is …
resampling algorithm) of particle filters is presented. First, the resampling algorithm is …