Name
Title | ||
|---|---|---|
Analysis of AIS Intermittency and Vessel Characterization using a Hidden Markov Model |
Abstract | ||
|---|---|---|
In this report we perform a statistical analysis of the Automated Identification System (AIS) communication channel. We base our study on a Hidden Markov Model (HMM). We model the transition between different states of the channel as a Markov Chain (MC); the probability that a message sent by a AIS transmitter will be lost is associated to each state. The MC is not directly observed, but the received time stamps of the AIS reports provide some statistical information about the current state of the channel as well as some information about the parameters of the model. Additionally, the statistic characteristics of the AIS channel are used in a batch anomaly detection algorithm that characterizes vessel as anomalous if their (hidden) transponder state is estimated to be in the off state for too high a fraction of the surveillance time. |
Year | Venue | Keywords |
|---|---|---|
2010 | GI-Jahrestagung | hidden markov model |
Field | DocType | Citations |
Maximum-entropy Markov model,Pattern recognition,Forward algorithm,Markov property,Markov model,Computer science,Markov chain,Communication channel,Variable-order Markov model,Artificial intelligence,Hidden Markov model | Conference | 2 |
PageRank | References | Authors |
0.51 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Guerriero | 1 | 180 | 15.11 |
| Stefano Coraluppi | 2 | 264 | 44.73 |
| Craig Carthel | 3 | 150 | 25.31 |
| PETER WILLETT | 4 | 3421 | 592.93 |