CINECA IRIS Institutional Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishIn this paper, we consider the problem of detecting the presence of a prospective, moving target from a set of noisy measurements. We propose a two-steps approach: The first step discards unreliable measurements (i.e., those whose likelihood ratio falls below a preassigned threshold); The second step, instead, exploits the correlation among observations taken at different time instants and makes the final decision. A novel, computationally efficient, track-before-detect algorithm which exploits the sparse nature of the measurements is proposed, and experimental results to asses the algorithm performance are provided.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
| Titolo: | A track-before-detect procedure for sparse data |
| Autori: | |
| Data di pubblicazione: | 2012 |
| Abstract: | In this paper, we consider the problem of detecting the presence of a prospective, moving target from a set of noisy measurements. We propose a two-steps approach: The first step discards unreliable measurements (i.e., those whose likelihood ratio falls below a preassigned threshold); The second step, instead, exploits the correlation among observations taken at different time instants and makes the final decision. A novel, computationally efficient, track-before-detect algorithm which exploits the sparse nature of the measurements is proposed, and experimental results to asses the algorithm performance are provided. |
| Handle: | http://hdl.handle.net/11580/23482 |
| ISBN: | 9781467301817 9781467301824 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
http://hdl.handle.net/11580/23482
Copyright © 2021