dc.contributor.author | Forti, Nicola | |
dc.contributor.author | Millefiori, Leonardo | |
dc.contributor.author | Braca, Paolo | |
dc.date.accessioned | 2019-06-18T13:18:41Z | |
dc.date.available | 2019-06-18T13:18:41Z | |
dc.date.issued | 2019/05 | |
dc.identifier.govdoc | CMRE-PR-2019-038 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12489/779 | |
dc.description.abstract | This paper presents a solution to the problem of sequential joint anomaly detection and tracking of a target subject to switching unknown path deviations. Based on a dynamic model described by Ornstein- Uhlenbeck (OU) stochastic processes, the anomaly is represented by a target (e.g., a marine vessel) that deviates from a preset route by changing its nominal mean velocity. The Random Finite Set (RFS) framework is used to represent the switching nature of target's anomalous behavior in the presence of spurious measurements and detection uncertainty. Combining these two ingredients, the problem of jointly detecting target's path deviations and estimating its kinematic state can be formulated within the Bayesian framework, and analytically solved by means of a hybrid Bernoulli filter that sequentially updates the joint posterior density of the unknown OU velocity input (a Bernoulli RFS) and of the target's state random vector. We illustrate the effectiveness of the proposed filter, implemented in Gaussian-mixture form, in a simulated scenario of vessel tracking for maritime traffic monitoring. | en_US |
dc.format | 7 p. : ill. ; digital, PDF file | en_US |
dc.language.iso | en | en_US |
dc.publisher | CMRE | en_US |
dc.source | In: Proceedings of the 21st International Conference on Information Fusion (FUSION 2018), Cambridge 2018, pp., 1178-1184 doi: 10.23919/ICIF.2018.8455567 | en_US |
dc.subject | Maritime surveillance | en_US |
dc.subject | Maritime security | en_US |
dc.subject | Maritime situational awareness | en_US |
dc.subject | Ship movements | en_US |
dc.subject | Ornstein-Uhlenbeck stochastic process | en_US |
dc.subject | Target tracking | en_US |
dc.subject | Ship tracking | en_US |
dc.subject | Radar | en_US |
dc.subject | Automatic Identification Systems (AIS) | en_US |
dc.subject | Shipping noise | en_US |
dc.subject | Trajectory estimation | en_US |
dc.subject | Random set theory | en_US |
dc.title | Hybrid Bernoulli filtering for detection and tracking of anomalous path deviations | en_US |
dc.type | Reprint (PR) | en_US |
dc.type | Papers and Articles | en_US |