dc.contributor.author | Millefiori, Leonardo | |
dc.contributor.author | Braca, Paolo | |
dc.contributor.author | Willett, Peter K. | |
dc.date.accessioned | 2019-06-19T10:38:24Z | |
dc.date.available | 2019-06-19T10:38:24Z | |
dc.date.issued | 2019/06 | |
dc.identifier.govdoc | CMRE-PR-2019-071 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12489/811 | |
dc.description.abstract | In this letter, we study the problem of estimating the long-run mean of the Ornstein-Uhlenbeck (OU) stochastic process and its effect on the long-term prediction of future vessel states, which is a crucial problem for Maritime Situational Awareness (MSA). We employ a sample mean estimator (SME) to estimate the key OU parameter from the observations, computing the closedform SME covariance error in both the random and constant sampling time regimes, providing a fundamental building block of the overall long-term state prediction covariance. We show also that the SME is: vn-consistent when the sampling time is random; asymptotically efficient when the sampling time is constant; and very close to the Cramer-Rao lower bound in the cases of practical interest for MSA. | en_US |
dc.format | 5 p. : ill. ; digital, PDF file | en_US |
dc.language.iso | en | en_US |
dc.publisher | CMRE | en_US |
dc.source | In: IEEE Signal Processing Letters, volume: 23, issue: 11, November 2016, pp. 1562-1566, doi: 10.1109/LSP.2016.2605705 | en_US |
dc.subject | Maritime security | en_US |
dc.subject | Maritime situational awareness | en_US |
dc.subject | Maritime surveillance | en_US |
dc.subject | Maritime route prediction | en_US |
dc.subject | Ornstein-Uhlenbeck stochastic process | en_US |
dc.title | Consistent estimation of randomly sampled Ornstein-Uhlenbeck process long-run mean for long-term target state prediction | en_US |
dc.type | Reprint (PR) | en_US |
dc.type | Papers and Articles | en_US |