Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Uney, Murat"'
Fusing and sharing information from multiple sensors over a network is a challenging task. Part of this challenge arises from the absence of a foundational rule for fusing probability distributions, with various approaches stemming from different pri
Externí odkaz:
http://arxiv.org/abs/2209.12245
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
In this work, we consider the detection of manoeuvring small objects with radars. Such objects induce low signal to noise ratio (SNR) reflections in the received signal. We consider both co-located and separated transmitter/receiver pairs, i.e., mono
Externí odkaz:
http://arxiv.org/abs/1709.00310
Multi-sensor state space models underpin fusion applications in networks of sensors. Estimation of latent parameters in these models has the potential to provide highly desirable capabilities such as network self-calibration. Conventional solutions t
Externí odkaz:
http://arxiv.org/abs/1708.00842
Autor:
Uney, Murat
In this thesis, we consider the problem of decentralized estimation under communication constraints in the context of Collaborative Signal and Information Processing. Motivated by sensor network applications, a high volume of data collected at distin
Externí odkaz:
http://etd.lib.metu.edu.tr/upload/12611226/index.pdf
Recent progress in multi-object filtering has led to algorithms that compute the first-order moment of multi-object distributions based on sensor measurements. The number of targets in arbitrarily selected regions can be estimated using the first-ord
Externí odkaz:
http://arxiv.org/abs/1310.2873
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Fusing and sharing information from multiple sensors over a network is a challenging task, especially in the context of multi-target tracking. Part of this challenge arises from the absence of a foundational rule for fusing probability distributions,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::898c499d9814003b5a391a3cee8d765d
http://arxiv.org/abs/2209.12245
http://arxiv.org/abs/2209.12245
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
IEEE transactions on Signal and Information Processing over Networks
IEEE transactions on Signal and Information Processing over Networks, IEEE, 2018, 4 (4), pp.752-768. ⟨10.1109/TSIPN.2018.2825599⟩
Uney, M, Mulgrew, B & Clark, D 2018, ' Latent parameter estimation in fusion networks using separable likelihoods ', IEEE Transactions on Signal and Information Processing Over Networks, vol. 4, no. 4, pp. 752-768 . https://doi.org/10.1109/TSIPN.2018.2825599
IEEE transactions on Signal and Information Processing over Networks, IEEE, 2018, 4 (4), pp.752-768. ⟨10.1109/TSIPN.2018.2825599⟩
Uney, M, Mulgrew, B & Clark, D 2018, ' Latent parameter estimation in fusion networks using separable likelihoods ', IEEE Transactions on Signal and Information Processing Over Networks, vol. 4, no. 4, pp. 752-768 . https://doi.org/10.1109/TSIPN.2018.2825599
Multi-sensor state space models underpin fusion applications in networks of sensors. Estimation of latent parameters in these models has the potential to provide highly desirable capabilities such as network self-calibration. Conventional solutions t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80befea7693e4fcdd40966675d619adf
https://hal.archives-ouvertes.fr/hal-01848809
https://hal.archives-ouvertes.fr/hal-01848809