Zobrazeno 1 - 7
of 7
pro vyhledávání: '"heavy-tailed measurement noise (HTMN)"'
Publikováno v:
IEEE Access, Vol 9, Pp 94438-94453 (2021)
A robust generalized labeled multi-Bernoulli (GLMB) filter is presented to perform multitarget tracking (MTT) with unknown non-stationary heavy-tailed measurement noise (HTMN). The HTMN is modeled as a multivariate Student’s t-distribution with unk
Externí odkaz:
https://doaj.org/article/09742fc148a84e5788c3ad47a4966c98
Publikováno v:
IEEE Access, Vol 9, Pp 94438-94453 (2021)
A robust generalized labeled multi-Bernoulli (GLMB) filter is presented to perform multitarget tracking (MTT) with unknown non-stationary heavy-tailed measurement noise (HTMN). The HTMN is modeled as a multivariate Student’s t-distribution with unk
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94654364c545a03cda4285296680e88d
https://doi.org/10.1109/access.2021.3092021
https://doi.org/10.1109/access.2021.3092021
Akademický článek
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Publikováno v:
Journal of Marine Science & Engineering; Jun2023, Vol. 11 Issue 6, p1243, 23p
Publikováno v:
Transactions of the Institute of Measurement & Control; May2022, Vol. 44 Issue 8, p1699-1707, 9p
Publikováno v:
Journal of Marine Science and Engineering; Volume 11; Issue 6; Pages: 1243
A suitable jump Markov system (JMS) filtering approach provides an efficient technique for tracking surface targets. In complex surface target tracking situations, due to the joint influences of lost measurements with an unknown probability and heavy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::750d70d1c4ecff32db936c8826227f0b
Publikováno v:
Transactions of the Institute of Measurement and Control. 44:1699-1707
A new robust Kalman filter (KF) based on mixing distribution is presented to address the filtering issue for a linear system with measurement loss (ML) and heavy-tailed measurement noise (HTMN) in this paper. A new Student’s t-inverse-Wishart-Gamma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ddcb8b50e248a71c577788fca1830c1
https://doi.org/10.1177/01423312211054942
https://doi.org/10.1177/01423312211054942