System and method for tracking and forecasting the positions of marine vessels
Abstract
There is disclosed a system and method for forecasting the positions of marine vessels. In an aspect, the present system is adapted to execute a forecasting algorithm to forecast the positions of one or a great many marine vessel(s) based on one or more position reporting systems including coastal and satellite AIS (S-AIS) signals or LRIT received from the vessel. The forecasting algorithm utilizes location and direction information for the vessel, and estimates one or more possible positions based on previous paths taken by vessels from that location, and heading in substantially the same direction. Thus, a body of water can be divided into “bins” of location and direction information, and a spatial index can be built based on the previous paths taken by other vessels after passing through that bin. Other types of information may also be taken into account, such as ship-specific data, nearby weather, ocean currents, the time of year, and other spatial variables specific to that bin.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A computer-implemented system for forecasting the position of a marine vessel based on one or more position reports, the system comprising:
at least one processor; and a memory storing one or more temporal position reports for a vessel comprising position and direction information, wherein the one or more temporal position reports are received from one or more sensors of a transponder system of the vessel, the memory comprising instructions which when executed by the at least one processor configure the at least one processor to:
determine one or more bins the vessel has travelled through by re-indexing the temporal position reports for the vessel using a spatial index comprising a plurality of bins, each bin containing location and direction information specific to each bin for each of a number of different directions such that the location and direction information specific to each bin is spatially indexed for each of the number of different directions; and
forecast a position of the vessel based on the spatial index and the location and direction information specific to the one or more bins through which the vessel has travelled by estimating one or more possible headings based on previous paths taken by other vessels from the location and heading in a substantially same direction as the vessel.
2 . The computer-implemented system of claim 1 , wherein the at least one processor is further configured to:
obtain contextual data about a surrounding ocean state; obtain current dynamic contextual information relating to the state of the vessel and the surrounding ocean state corresponding to the information related to each bin; and forecast the position of the vessel based on the spatial index, the location and direction information specific to the one or more bins through which the vessel has travelled, and the comparison of contextual information stored in one or more bins with that of the current dynamic situation of the vessel and the surrounding ocean state.
3 . The computer-implemented system of claim 1 , wherein the at least one processor is further configured to detect an unexpected position of a marine vessel based on a comparison of each new position report combined with a stored history of the statistical accuracy of the forecasted expected position of the specific vessel by computing and updating the statistical accuracy of recent forecasts upon receipt of each new position report.
4 . The computer-implemented system of claim 3 , wherein the statistical accuracy includes a measure of a median absolute deviation.
5 . The computer-implemented system of claim 3 , wherein the at least one processor is further configured to:
store a per-vessel statistical forecasting accuracy for each vessel; and compare each new position report with the statistical forecasting accuracy for that vessel and determines if the new position is sufficiently different from the expected position that it exceeds a predefined threshold constituting an anomalous position.
6 . The computer-implemented system of claim 1 , wherein the at least one processor is further configured to:
detect an unexpected failure to receive a position report from a marine vessel based on a comparison of the time since the previous position report with a stored history of the statistical periodicity of message receipt for that specific vessel; update the statistical accuracy of the time between receipt of position reports upon receipt of each new position report. compare the time since the prior position report with the statistical time periodicity of message receipt for that vessel; determine if the new time difference is sufficiently different from the expected time that it exceeds a predefined threshold constituting an anomalous position; monitor the time since receipt of the message for one or more ships; and compare the elapsed time to the stored statistical periodicity of message receipt for that vessel and determines if the elapsed time is sufficiently different from the expected time that it exceeds a predefined threshold constituting an anomalous position.
7 . The system of claim 1 , wherein the at least one processor is further to generate a visualization of the forecasted position of the vessel and transmit the visualization to a display device to display the forecasted position of the vessel.
8 . A computer-implemented method for forecasting the position of a marine vessel based on one or more position reports, comprising:
receiving, at a repository of a marine vessel tracking system, one or more temporal position reports for a vessel comprising position and a direction information, wherein the one or more temporal position reports are received from one or more sensors of a transponder system of the vessel; determining one or more bins the vessel has travelled through by re-indexing the temporal position reports for the vessel using a spatial index comprising a plurality of bins, each bin containing location and direction information specific to each bin for each of a number of different directions such that the location and direction information specific to each bin is spatially indexed for each of the number of different directions; and forecasting a position of the vessel based on the spatial index and the location and direction information specific to the one or more bins through which the vessel has travelled by estimating one or more possible headings based on previous paths taken by other vessels from the location and heading in a substantially same direction as the vessel.
9 . The computer-implemented method of claim 8 , further comprising:
obtaining contextual data about a surrounding ocean state; obtaining current dynamic contextual information relating to the state of the vessel and the surrounding ocean state corresponding to the information related to each bin; and forecasting the position of the vessel based on the spatial index, the location and direction information specific to the one or more bins through which the vessel has travelled, and the comparison of contextual information stored in one or more bins with that of the current dynamic situation of the vessel and the surrounding ocean state.
10 . The computer-implemented method of claim 9 , further comprising:
detecting an unexpected position of a marine vessel based on a comparison of each new position report combined with a stored history of the statistical accuracy of the forecasting algorithms for that specific vessel by computing and updating the statistical accuracy of recent forecasts upon receipt of each new position report.
11 . The computer-implemented method of claim 10 , wherein the statistical accuracy includes a measure of a median absolute deviation.
12 . The computer-implemented method of claim 10 , further comprising storing a per-vessel statistical forecasting accuracy for each vessel.
13 . The computer-implemented method of claim 12 , further comprising:
comparing each new position report with the statistical forecasting accuracy for that vessel; and determining if the new position is sufficiently different from the expected position that it exceeds a predefined threshold constituting an anomalous position.
14 . The computer-implemented method of claim 8 , further comprising:
detecting an unexpected failure to receive a position report from a marine vessel based on a comparison of the time since the previous position report with a stored history of the statistical periodicity of message receipt for that specific vessel; and updating the statistical accuracy of the time between receipt of position reports upon receipt of each new position report.
15 . The computer-implemented method of claim 14 , further comprising:
comparing the time since the prior position report with the statistical time periodicity of message receipt for that vessel; and determining if the new time difference is sufficiently different from the expected time that it exceeds a predefined threshold constituting an anomalous position.
16 . The computer-implemented method of claim 15 , further comprising:
monitoring the time since receipt of the message for one or more ships; and comparing the elapsed time to the stored statistical periodicity of message receipt for that vessel and determines if the elapsed time is sufficiently different from the expected time that it exceeds a predefined threshold constituting an anomalous position.
17 . The computer-implemented method of claim 8 , further comprising:
displaying a visualization of the forecasted position of the vessel at a display device.
18 . A computer-implemented method for disambiguating Automatic Identification System (AIS) transmissions from different vessels using the same Maritime Mobile Service Identity (MMSI) identifier, the method comprising:
receiving, at a repository of a marine vessel tracking system, one or more temporal position reports for combined vessel identifiers from transponder systems of the marine vessels, each combined vessel identifier comprising:
a MMSI identifier comprising temporal position, heading, speed and timestamp data associated with a marine vessel; and
an additional identifier associated with the marine vessel;
receiving, at the marine vessel tracking system from a transponder system of a first vessel, a new position report for a first MMSI identifier;
for each combined vessel identifier associated with the stored temporal position reports that include the first MMSI identifier, determining, at the marine vessel tracking system, a probability value that the new position report is associated with that combined vessel identifier;
assigning, at the marine vessel tracking system, a probable combined vessel identifier to the new position report based on the determined probability values; and
distinguishing between marine vessels broadcasting a same MMSI identifier using their respective combined vessel identifiers.
19 . The computer-implemented method of claim 18 , wherein the probable combined vessel identifier comprises an existing combined vessel identifier associated with a temporal report having a highest determined probability value.
20 . The computer-implemented method of claim 18 , wherein the probable combined vessel identifier comprises a new combined vessel identifier when there is no probability match between a stored temporal report and an existing combined vessel identifier having the first MMSI identifier, the new combined vessel identifier comprising the first MMSI identifier and a new additional vessel identifier, wherein a positional probability report of a vessel is determined in part by at least one of:
the position; the heading; the speed; or the timestamp data; of the one or more temporal position reports for that vessel.Join the waitlist — get patent alerts
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