Spirent Communications, Inc.

Matthew Pottle

Paul Hansen

Jeremy Charles Bennington

Esther Anyaegbu

Richard West

Colin Richard Ford

Ronald Toh Ming Wong

Samuel Nardoni

Disclosed is route planning using a worst-case risk analysis and, if needed, a best-case risk analysis of GNSS coverage. The worst-case risk analysis identifies cuboids or 2d regions through which a vehicle can be routed with assurance that adequate GNSS coverage will be available regardless of the time of day that the vehicle travels. The best-case risk analysis identifies cuboids or 2d regions through which there is adequate coverage at some times during the day. In case path finding using the worst-case risk analysis fails, a best-case risk analysis can be requested and used to find alternate potential path(s). Time dependent forecast data that covers regions along the alternate potential path(s) can be requested and used to route vehicles, including autonomous drones, from starting points to destinations. This includes generation, distribution and use of risk analysis data, implemented as methods, systems and articles of manufacture.

Utilizing GNSS risk analysis data for facilitating safe routing of autonomous drones

Disclosed is a method of detecting and rejecting a spoofing or jamming signal source by receiving at a first device a forecast of a visibility for each Global Navigation Satellite System (GNSS) satellite signal source in the forecast at a GNSS receiver coupled to the first device, calculating from at least an elevation and the received visibility of the satellite signal sources in the forecast a predicted Signal to Noise Ratio (SNR), comparing SNR acquired by the GNSS receiver of one or more of the satellite signal sources to the predicted SNR, detecting a spoofing signal source based on acquiring a higher SNR than predicted or a jamming signal source based on acquiring a lower SNR than predicted, and rejecting the spoofing or jamming signal source based on differences between the acquired and predicted SNR.

GNSS forecast and spoofing/jamming detection

Disclosed is representing distant objects for analysis of satellite line-of-sight visibility from a grid of points by constructing a first 3D model of foreground objects that obscure line-of-sight visibility of satellites from a grid of points, wherein the first 3D model is at a first resolution, where spacing of grid points denotes obstruction edges, constructing a second 3D model of background objects that are more than a threshold distance away and that object obscure line-of-sight visibility of satellites from the grid of points, wherein the second 3D model is at a second resolution that is different from and coarser than the first resolution, calculating a line-of-sight visibility of the satellites from the grid of points using a combination of the first and second 3D models, and responding to a query for an area by providing the calculated line-of-sight visibility of the satellites for points of the grid within the area.

GNSS forecast and background obscuration prediction

Generating and distributing GNSS risk analysis data for facilitating safe routing of autonomous drones

Disclosed is determining GNSS satellite position visibility by possessing an orbital segment representing the transit of a satellite in orbit over time, a coarse ray angle interval, a fine ray angle interval, and a digital surface model. Disclosed is propagating coarse ray at coarse ray angle intervals increments in a first pass between an observable point and orbital segment at a respective coarse ray angle to determine whether the coarse ray is obstructed by features of the DSM, and recording a status of the coarse ray based on whether the coarse ray was obstructed. If pairs of successive coarse rays have different status, designating the coarse ray with NLOS visibility, then performing a second pass by propagating, per each designated coarse ray, fine rays at fine ray angle intervals, and saving an indication of time at which LOS visibility to the satellite is obstructed.

GNSS forecast and line of sight detection

Disclosed is reducing starting time for a GNSS receiver that has an imprecise initial starting location by requesting starting assistance from a CDN that caches predictive data including first data indicated predicted LOS visibility from the receiver to individual satellites, wherein the request includes the imprecise initial staring location, receiving, from the CDN, data that includes a first block of the predictive data for the imprecise initial staring location and further adjoining second blocks of predictive data for areas surrounding the imprecise staring location, determining, by the GNSS receiver, commonly available satellites that have visibility from locations in both the first block and the second block, and calculating a first starting position using weighted values for the satellites, the commonly available satellites having higher weighted value than satellites without visibility in both locations, whereby position uncertainty of the first starting position is reduced from the imprecise initial starting location.

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Paignton, United Kingdom

Matthew Pottle