首页|Patent Issued for Seamlessly tracking a water vessel using satellite and mobile data (USPTO 11940543)

Patent Issued for Seamlessly tracking a water vessel using satellite and mobile data (USPTO 11940543)

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The following quote was obtained by the news editors from the background informa tion supplied bythe inventors: “While it is possible to locate users on land wi th a high level of accuracy, this is a resultof having multiple dedicated syste ms that work together to triangulate land location, such as GlobalPositioning S ystem (GPS), mobile cell towers, and additional sensors. This level of accuracy cannot beachieved off-shore because these systems do not exist off-shore. In or der to have their locations tracked,larger commercial water vessels may have de dicated location technology that triggers a location to be sentvia satellite to a tracking tool periodically. These water vessels can share their location info rmation witha central server for tracking purposes. However, these location tra cking mechanisms are computationallyand practically expensive, and thus locatio ns are sent infrequently (e.g., every 15 minutes). While thisinfrequently updat ed location information may be effective on the open ocean, they may not be as effective near-shore (e.g., within twenty miles from shore) as more granular upda te may be needed forcommercial purposes, mariner safety, and rescue purposes. T his results in a hampered ability to trackcommercial water vessels for providin g updates on a central server, to provide a more frequent locationupdate of the water vessels near-shore to improve mariner safety, and to find mariners who ma y be indistress, thus resulting in failed rescue efforts where a distressed mar iner cannot be found. Moreover,smaller (typically personal) water vessels often do not have these satellite tracking mechanisms, and evenif they did, they ten d to travel at faster speeds that would make their trajectory impossible to calc ulatewith such infrequent location markers. This results in a blind spot for re scue agencies regarding smallerwater vessels, having little to no data or old d ata of smaller water vessels.

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2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Apr.15)