NDLI: Distributed track-to-track fusion for non-linear systems with Gaussian mixture noise

Content Provider	IET Digital Library
Author	Lu, Kelin Sun, Changyin Fu, Qien Zhu, Qian
Abstract	The authors propose a distributed state estimation algorithm based on optimal track-to-track fusion for local posteriors in terms of Gaussian mixtures. The track-to-track fusion system is implemented with both parallel and sequential structure based on generalised covariance intersection rule. They obtain the optimal fusion coefficients in a computationally efficient manner via Monte Carlo importance sampling method. The Dirac mixture approximation method is proposed for the computation of arbitrary power of a Gaussian mixture density. The resulting Gaussian mixture fusion rule is analytical and applicable to the multi-sensor case. Numerical examples are presented to demonstrate the performance advantages of the proposed method in comparison with existing track-to-track fusion algorithms.
Starting Page	740
Ending Page	749
Page Count	10
ISSN	17518784
Volume Number	13
e-ISSN	17518792
Issue Number	Issue 5, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.5186
Journal	IET Radar, Sonar & Navigation
Publisher Date	2019-01-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Approximation Theory Covariance Analysis Dirac Mixture Approximation Method Distributed State Estimation Algorithm Distributed Track-to-track Fusion Gaussian Distribution Gaussian Mixture Density Gaussian Mixture Fusion Rule Gaussian Mixture Noise Gaussian Processes Generalised Covariance Intersection Rule Importance Sampling Interpolation And Function Approximation Local Posteriors Monte Carlo Importance Sampling Method Monte Carlo Method Nonlinear System Numerical Analysis Optimal Fusion Coefficient Parallel Structure Sensor Fusion Sequential Structure Signal Processing And Detection Signal Processing Theory Signal Sampling State Estimation Track-to-track Fusion Algorithm Track-to-track Fusion System
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

Heterogeneous sensor fusion based on Copula theory and importance sampling

Practical method to predict an upper bound for minimum variance track-to-track fusion

Optimal distributed Kalman filtering fusion for multirate multisensor dynamic systems with correlated noise and unreliable measurements

A novel distributed fusion algorithm for multi-sensor nonlinear tracking

Adaptive Gauss–Hermite filter for non-linear systems with unknown measurement noise covariance

Importance Sampling Kalman Filter for Image Estimation

Radar/ESM anti-bias track association algorithm based on track distance vector detection

Distributed multi-sensor fusion using generalised multi-Bernoulli densities

State estimation for networked systems with a Markov plant in the presence of missing and quantised measurements

Distributed track-to-track fusion for non-linear systems with Gaussian mixture noise

Similar Documents

Heterogeneous sensor fusion based on Copula theory and importance sampling

Practical method to predict an upper bound for minimum variance track-to-track fusion

Optimal distributed Kalman filtering fusion for multirate multisensor dynamic systems with correlated noise and unreliable measurements

A novel distributed fusion algorithm for multi-sensor nonlinear tracking

Adaptive Gauss–Hermite filter for non-linear systems with unknown measurement noise covariance

Importance Sampling Kalman Filter for Image Estimation

Radar/ESM anti-bias track association algorithm based on track distance vector detection

Distributed multi-sensor fusion using generalised multi-Bernoulli densities

State estimation for networked systems with a Markov plant in the presence of missing and quantised measurements

Distributed track-to-track fusion for non-linear systems with Gaussian mixture noise