Architecture for providing forecasts of low earth orbit (LEO) satellite obscuration and multipath
Disclosed is a method of providing DOP forecasts for LEO navigation for routing of vehicles, aircraft, alerting humans in vehicles, or wireless devices, and bandwidth forecasts for LEO communications. The method includes accessing a 3D map of an area including structure solids and generating cuboids in spaces not contained in the structure solids; and iteratively over time increments, calculating LEO satellites visible from the cuboids using the map and, using at least the calculated visibility, determining forecasts for the cuboids at the time increments. Also included is compressing the determined forecast spatially and temporally; and distributing the compressed DOP forecast via a CDN, responsive to queries from requestors. Systems of the requestors can take into account the forecast for routing vehicles or alerting humans in vehicles to a predicted navigation impairment. Risk analysis is applied to improving computation and distribution of forecasts. Forecasts are applied to satellite deployment.
1 . A method of providing dilution of precision (abbreviated DOP) forecasts for Low Earth Satellite (LEO) constellations providing Position Navigation and Time (PNT) signals or Non Terrestrial Networking (NTN) Communications signals, collectively referred to as LEO signals, used for navigation for routing of vehicles, aircraft, alerting humans in vehicles, or wireless devices, including:
accessing a 3D map of an area including structure solids and generating cuboids in spaces not contained in the structure solids;
iteratively over time increments, calculating LEO satellites visible from the cuboids using the 3D map and, using at least the calculated visibility, determining a DOP forecast for LEO signals observable in the cuboids at the time increments;
compressing the determined DOP forecast spatially and temporally; and
distributing the compressed DOP forecast via a content delivery network (abbreviated CDN), responsive to queries from requestors to an API of the CDN, whereby systems of the requestors can take into account the DOP forecast for routing the vehicles or alerting the humans in the vehicles to a predicted navigation impairment.
2 . The method of claim 1 , wherein the LEO signal is a LEO-NTN signal, wherein the DOP forecast takes into account doppler shifts of LEO signal based the LEO satellites visible, based on elevation above the horizon of respective LEO satellites.
3 . The method of claim 1 , wherein the LEO signal is a LEO-PNT signal.
4 . The method of claim 1 , wherein the area is an urban environment and the solid structures include buildings of ten floors or more.
5 . The method of claim 1 , further including applying trigonometric triangulation factors when determining the dilution of precision forecasts for LEO PNT navigation using the LEO satellites visible by ray casting using predicted shadow masks from the cuboids.
6 . The method of claim 1 , further including calculating and distributing a degree of confidence measure with the dilution of precision forecasts of the LEO satellites.
7 . The method of claim 1 , further including forecasting a degree of multipathing, using ray casting or ray tracing between the satellites, reflective surfaces, and the cuboids, taking into account reflections and distributing the forecast degree of multipathing via the content delivery network.
8 . The method of claim 1 , further including forecasting multipathing of signals from particular satellites, using ray casting or ray tracing between the satellites, reflective surfaces, and the cuboids, and taking into account multipath reflections in the dilution of precision forecasts.
9 . The method of claim 1 , further including forecasting multipathing of signals from particular satellites, using ray casting or ray tracing between the satellites, reflective surfaces, and the cuboids, and distributing information about the forecasting of signal from the particular satellites via the content delivery network.
10 . The method of claim 1 , wherein the vehicles are wheeled vehicles, further including the 3D map including streets and limiting the cuboids to streets navigable by the wheeled vehicles.
11 . The method of claim 1 , wherein the vehicles are wheeled vehicles, that are autonomous capable, and that further have a driver onboard, further including:
using a navigation component onboard the vehicle:
looking ahead along a path the vehicle is following;
determining that a degraded LEO navigation dilution of precision will be encountered; and
alerting the driver at least a predetermined time before the encounter.
12 . The method of claim 1 , wherein the vehicles are flying taxis, autonomous capable, and that further have a pilot onboard, further including:
using a navigation component onboard the autonomous taxi:
looking ahead along a path the taxi is following;
determining that a degraded LEO navigation dilution of precision will be encountered; and
alerting the pilot at least a predetermined time before the encounter.
13 . A non-transitory computer readable storage medium loaded with program instructions that, when executed on processors, cause the processors to become operable to perform a method as set out in claim 1 .
14 . A non-transitory computer readable storage medium loaded with program instructions that, when executed on processors, cause the processors to become operable to perform a method as set out in claim 11 .
15 . A system comprising one or more computing devices with processors, memory coupled to the processors and computer instructions from the non-transitory computer readable storage medium of claim 13 loaded into the memory, configured to perform the method of relaying dilution of precision forecasts.
16 . A method of relaying dilution of precision forecasts for Low Earth Satellite (LEO) constellations providing Position Navigation and Time (PNT) signals or Non Terrestrial Networking (NTN) Communications signals, collectively referred to as LEO signals, for routing of vehicles or alerting humans in vehicles, including:
receiving at a content delivery network (abbreviated CDN) a compressed dilution of precision forecast, wherein the compressed dilution of precision forecast represents:
for cuboids in spaces not contained in structure solids represented on a 3D map of an area;
iterative calculations over time increments, of LEO satellites visible from the cuboids using the 3D map, incorporated into the dilution of precision forecast for the cuboids at the time increments;
receiving at an API to the CDN, queries from requestors; and
responding to the queries by providing responsive parts of the dilution of precision forecast;
whereby systems of the requestors can take into account the dilution of precision forecast for routing the vehicles or alerting the humans in the vehicles to a predicted navigation impairment.
17 . The method of claim 16 , further including the CDN receiving and distributing a degree of confidence measure with the dilution of precision forecasts responsive to the queries.
18 . The method of claim 16 , further including, responsive to a first query to the API, providing a manifest of available files responsive to the first query, then receiving a second query that selects some or all of the available files, and then responding to the second query by providing selected files, from the available files, containing the dilution of precision forecast.
19 . A non-transitory computer readable storage medium loaded with program instructions that, when executed on processors, cause the processors to become operable to perform a method as set out in claim 13 .
20 . A non-transitory computer readable storage medium loaded with program instructions that, when executed on processors, cause the processors to become operable to perform a method as set out in claim 13 .
21 . A system comprising one or more computing devices with processors, memory coupled to the processors and computer instructions from the non-transitory computer readable storage medium of claim 19 loaded into the memory, configured to perform the method of relaying dilution of precision forecasts.