Journal ArticleDOI
Particle Filtering for State Estimation in Nonlinear Industrial Systems
TLDR
It is shown that, in this kind of sensor fusion problem, the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations.Abstract:
State estimation is a major problem in industrial systems, particularly in industrial robotics. To this end, Gaussian and nonparametric filters have been developed. In this paper, the extended Kalman filter, which assumes Gaussian measurement noise, is compared with the particle filter, which does not make any assumption on the measurement noise distribution. As a case study, the estimation of the state vector of an industrial robot is used when measurements are available from an accelerometer that was mounted on the end effector of the robotic manipulator and from the encoders of the joints' motors. It is shown that, in this kind of sensor fusion problem, the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations.read more
Citations
More filters
Journal ArticleDOI
PALDi: Online Load Disaggregation via Particle Filtering
TL;DR: This paper evaluates the PF-based NILM approach on synthetic and on real data from a well-known dataset to show that the approach achieves an accuracy of 90% on real household power draws.
Journal ArticleDOI
Remaining Useful Life Prediction of Rolling Bearings Using an Enhanced Particle Filter
Yuning Qian,Ruqiang Yan +1 more
TL;DR: An enhanced particle filter approach for predicting remaining useful life (RUL) of rolling bearings is presented and it can achieve better performance than the traditional PF-based approach and commonly used support vector regression approach.
Journal ArticleDOI
A Derivative-Free Kalman Filtering Approach to State Estimation-Based Control of Nonlinear Systems
TL;DR: The proposed derivative-free Kalman filtering approach is suitable for state estimation-based control of a class of nonlinear systems without the need for derivatives and Jacobians calculation and without using linearization approximations.
Journal ArticleDOI
Bearing Degradation Evaluation Using Recurrence Quantification Analysis and Kalman Filter
TL;DR: An integrated approach, which combines recurrence quantification analysis (RQA) with the Kalman filter, for bearing degradation evaluation is presented, which can predict occurrence of the bearing failure 50 min in advance.
Journal ArticleDOI
Automatic Crack Detection and Measurement Based on Image Analysis
TL;DR: A system based on machine vision concepts has been developed with the goal to automate the crack measurement process using only a single camera installed in a truck or even in a robot, and the crack dimensions are estimated.
References
More filters
Journal ArticleDOI
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
BookDOI
Sequential Monte Carlo methods in practice
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Book
Probabilistic Robotics
TL;DR: This research presents a novel approach to planning and navigation algorithms that exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles.
Book
Beyond the Kalman Filter: Particle Filters for Tracking Applications
TL;DR: Part I Theoretical concepts: introduction suboptimal nonlinear filters a tutorial on particle filters Cramer-Rao bounds for nonlinear filtering and tracking applications: tracking a ballistic object bearings-only tracking range- only tracking bistatic radar tracking targets through blind Doppler terrain aided tracking detection and tracking of stealthy targets group and extended object tracking.