Extended Target Tracking in Human–Robot Coexisting Environments via Multisensor Information Fusion: A Heteroscedastic Gaussian Process Regression-Based Approach-Reference-Cited by-同舟云学术

Extended Target Tracking in Human–Robot Coexisting Environments via Multisensor Information Fusion: A Heteroscedastic Gaussian Process Regression-Based Approach

Published:2023-09 Issue:9 Volume:19 Page:9877-9886
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Paral Pritam¹^ORCID,Chatterjee Amitava²^ORCID,Rakshit Anjan²^ORCID,Pal Sankar K.¹^ORCID

Affiliation:

1. Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India

2. Department of Electrical Engineering, Jadavpur University, Kolkata, India

Funder

Ministry of Electronics and IT, Government of India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10192510/10018511.pdf?arnumber=10018511

Reference27 articles.

1. A cointegration approach for heteroscedastic data based on a time series decomposition: An application to structural health monitoring

2. Retrieval of Biophysical Parameters With Heteroscedastic Gaussian Processes

3. Divisive Gaussian Processes for Nonstationary Regression

4. Regression with input-dependent noise: A Gaussian process treatment;goldberg;Proc Adv Neural Inf Process Syst,0

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