NDLI: Unscented Kalman filter-aided Gaussian sum filter

Please wait, while we are loading the content...

Improvement of time difference of arrival measurements resolution by using fractional delay filters in a direct sequence-code division multiple access radionavigation system

Joint inference of dominant scatterer locations and motion parameters of an extended target in high range-resolution radar

Model-switched Gaussian sum cubature Kalman filter for attitude angle-aided three-dimensional target tracking

Novel compressive sensing-based Dechirp-Keystone algorithm for synthetic aperture radar imaging of moving target

Parameter estimation of manoeuvring targets based on segment integration and Lv's transform

Radiolocation and tracking of automatic identification system signals for maritime situational awareness

Re-entry vehicle tracking with a new multiple model estimation applicable to highly non-linear dynamics

Unscented Kalman filter-aided Gaussian sum filter

Volume 9 (2015) : Issue 4, Apr

Volume 9 (2015) : Issue 3, Mar

Volume 9 (2015) : Issue 2, Feb

Volume 9 (2015) : Issue 1, Jan

IET Renewable Power Generation

IET Science, Measurement & Technology

IET Signal Processing

IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

Unscented Kalman filter-aided Gaussian sum filter

Content Provider	IET Digital Library
Author	Gokce, Murat Kuzuoglu, Mustafa
Abstract	A non-linear filter is developed for continuous-time systems with observations/measurements carried out in discrete-time. The filter developed can approximate the a priori and a posteriori probability density function (pdf) with weighted Gaussian sums inside specific search regions. To make the approximations, first, Gaussians are placed with equal intervals inside the search regions and deterministic sample points are chosen within the search regions. The pdf values are then calculated at sample points using numerical solution of the Fokker–Planck equation for the a priori pdf and using Bayes’ rule for the a posteriori pdf. These values are used to find the weights of Gaussians using least-squares method. This process is similar to curve fitting with Gaussian radial basis functions. Inside the search regions, locations of the sample points and mean and covariance values of Gaussians are found by the help of a unscented Kalman filter (UKF). By adjusting the width of the search regions, all the parts, or the ones close to mean values of pdfs, can be approximated. The performance of the filter developed is analysed using a non-linear system with a single-state variable and two radar tracking applications. It is compared with particle filter, UKF and converted measurement Kalman filter for these cases.
Starting Page	589
Ending Page	599
Page Count	11
ISSN	17518784
Volume Number	9
e-ISSN	17518792
Issue Number	Issue 5, Jun (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/9/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2014.0088
Journal	IET Radar, Sonar & Navigation
Publisher Date	2015-02-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Rule Curve Fitting Filtering Method in Signal Processing Fokker–Planck Equation Gaussian Processes Gaussian Radial Basis Function Interpolation And Function Approximation Kalman Filter Least Squares Approximation Least-squares Method Nonlinear Filter Nonlinear System Numerical Analysis Particle Filter Posteriori Pdf Probability Probability Density Function Radar Tracking Application Search Region Signal Processing Theory Single-state Variable Statistics UKF Unscented Kalman Filter-aided Gaussian Sum Filter Weighted Gaussian Sums
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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

Linear fitting Kalman filter

Gaussian sum filter of Markov jump non-linear systems

Alternative framework for the iterated unscented Kalman filter

Hybrid method for power system state estimation

Extended target probability hypothesis density filter based on cubature Kalman filter

Bayesian step least mean squares algorithm for Gaussian signals

UKF Based Nonlinear Filtering Using Minimum Entropy Criterion

Estimation of state-of-charge based on unscented Kalman particle filter for storage lithium-ion battery

Fault detection for non-linear system with unknown input and state constraints

Unscented Kalman filter-aided Gaussian sum filter

Similar Documents

Linear fitting Kalman filter

Gaussian sum filter of Markov jump non-linear systems

Alternative framework for the iterated unscented Kalman filter

Hybrid method for power system state estimation

Extended target probability hypothesis density filter based on cubature Kalman filter

Bayesian step least mean squares algorithm for Gaussian signals

UKF Based Nonlinear Filtering Using Minimum Entropy Criterion

Estimation of state-of-charge based on unscented Kalman particle filter for storage lithium-ion battery

Fault detection for non-linear system with unknown input and state constraints

Unscented Kalman filter-aided Gaussian sum filter