NDLI: A New Nonlinear Filter Algorithm Based on QMC Quadrature

Content Provider	IEEE Xplore Digital Library
Author	Huang Dong-min Pan Quan
Copyright Year	2008
Description	Author affiliation: Dept. of Autom. Control, Northwestern Polytech. Univ, Xi'an (Huang Dong-min; Pan Quan)
Abstract	In order to avoid the possible gaps and clusters that arise from random sampling in Monte Carlo (MC) methods, and improve the sampling efficiency and calculation accuracy, the Quasi-Monte Carlo (QMC) methods are to be applied to replace it. The idea in QMC is to use more regularly distributed and deterministic points for sampling an integrand. We propose a new nonlinear filter by applying the QMC sampling methods to the particle filter algorithm. Given certain proposal distributions, a simulation example is presented. The results show that the nonlinear filter based on the QMC methods performs more efficient than that based on the MC methods. The performance provides some references for the real-time application of particle filter in nonlinear / non-Gaussian systems.
Starting Page	190
Ending Page	193
File Size	263043
Page Count	4
File Format	PDF
ISBN	9780769533360
DOI	10.1109/CSSE.2008.1082
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear equations Nonlinear filters interval estimation Nonlinear dynamical systems particle filter Convergence Computer science Monte Carlo methods Monte Carlo low-discrepancy sequences Sampling methods Particle filters Quasi-Monte Carlo Nonlinear systems State estimation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Truncated particle filter based on QMC sampling and application to target tracking

Convergence Properties of Particle Filter Algorithm

Improved Quasi-Monte-Carlo particle filter algorithm Based on GRNN

Quasi-Monte Carlo filtering in nonlinear dynamic systems

Importance Sampling to Accelerate the Convergence of Quasi-Monte Carlo (2007)

A new quasi-Monte Carlo filtering algorithm based on number theoretical method

A Particle Swarm Optimized Particle Filter for Nonlinear System State Estimation

Sampling Densities of Particle Filter: A Survey and Comparison

A new unscented particle filter

A New Nonlinear Filter Algorithm Based on QMC Quadrature

Similar Documents

Truncated particle filter based on QMC sampling and application to target tracking

Convergence Properties of Particle Filter Algorithm

Improved Quasi-Monte-Carlo particle filter algorithm Based on GRNN

Quasi-Monte Carlo filtering in nonlinear dynamic systems

Importance Sampling to Accelerate the Convergence of Quasi-Monte Carlo (2007)

A new quasi-Monte Carlo filtering algorithm based on number theoretical method

A Particle Swarm Optimized Particle Filter for Nonlinear System State Estimation

Sampling Densities of Particle Filter: A Survey and Comparison

A new unscented particle filter

A New Nonlinear Filter Algorithm Based on QMC Quadrature