Reduced‐order multisensory fusion estimation with application to object tracking

Reduced‐order multisensory fusion estimation with application to object tracking

Abstract This paper investigates the track‐to‐track state estimation for a class of linear time‐varying multisensory systems. We propose a novel low‐complexity reduced‐order filter (ROF) under the Kalman filtering framework. Unlike the majority of previous track‐to‐track strategies, the proposed fus...

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Bibliographic Details
Main Authors: Vladimir Shin, Vahid Hamdipoor, Yoonsoo Kim
Format: Article
Language:English
Published: Hindawi-IET 2022-06-01
Series:IET Signal Processing
Subjects:
covariance intersection
Kalman filtering
multisensory system
reduced‐order filter
state estimation
track‐to‐track fusion
Online Access:https://doi.org/10.1049/sil2.12120

Internet

https://doi.org/10.1049/sil2.12120

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