Spirent Communications, Inc.

Colin Richard Ford

Paul Hansen

Jeremy Charles Bennington

Esther Anyaegbu

Ronald Toh Ming Wong

Richard West

Rafal Zbikowski

Adam Gleave

Raphael Grech

Dennis Berres

Arthur Edward Neeves

Matthew Pottle

Samuel Nardoni

Mark Geoffrey Holbrow

John Pottle

Guy Buesnel

Jason Davis

Kunal Shanishchara

Ram Garapaty

Brock Butler

Manikantan Parameswaran

Steve Hickling

Mark Hunter

Neil Bennett

Daniel Martin

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

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

The disclosed technology teaches testing an autonomous vehicle: shielding a GNSS receiving antenna of the vehicle from ambient GNSS signals while the vehicle is under test and supplanting the ambient GNSS signals with simulated GNSS signals. Testing includes using a GNSS signal generating system: receiving the ambient GNSS signals using an antenna of the system and determining a location and acceleration of the vehicle from the GNSS signals, accessing a model of an augmented environment that includes multi-pathing and obscuration of the GNSS signals along a test path, based on the determined location—generating the simulated GNSS signals to feed to the vehicle, in real time—simulating at least one constellation of GNSS satellite sources modified according to the augmented environment, based on the determined location, and feeding the simulated signals to a receiver in the vehicle, thereby supplanting ambient GNSS as the autonomous vehicle travels along the test path.

Systems and methods for simulating GNSS multipath and obscuration with networked autonomous vehicles

The technology disclosed teaches a method of improving accuracy of a GNSS receiver that has a non-directional antenna, with the receiver sending CDN a request for predictive data for an area that includes the receiver. Responsive to the query, the method includes receiving data regarding LOS visibility for the receiver with respect to individual satellites, and the receiver using the data for satellite selection, for choosing some and ignoring other individual satellites. Also disclosed is using the data to exclude from satellite selection at least one individual satellite based on lack of LOS visibility to the individual satellite. Further disclosed is recognizing and rejecting spoofed GNSS signals received by a GNSS receiver that has a non-directional antenna, in response to a CDN response to a request for predictive data for an area that includes the receiver, with the receiver comparing the data with measures of signals received from individual satellites.

Accuracy of a GNSS receiver that has a non-directional antenna

The technology disclosed teaches a method of path planning using a GNSS Forecast, requesting the GNSS Forecast of signal obscuration on behalf of a vehicle travelling in a region, receiving and using the Forecast to plan a path or route that has GNSS signals available over the path or route that satisfy a predetermined criterium. Also taught are GNSS Forecasts and planned paths or routes for a plurality of flying vehicles used by a flight control system, requesting the GNSS Forecast of signal obscuration on behalf of a flying autonomous or automated vehicle travelling in a region, receiving and using the Forecast and to plan a path with GNSS signals available over the path that satisfy predetermined criteria including accommodating real-time changes in flight paths, without leaving space, that satisfies the predetermined criteria. Also taught is certifying performance of GNSS receivers used on a flying vessel.

Path planning using forecasts of obscuration and multipath

Disclosed is a method of providing dilution of precision (DOP) forecasts for GNSS navigation and optionally degree of confidence, for routing of vehicles or alerting humans in vehicles: 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 GNSS satellites visible from the cuboids using the 3D map and, using at least the calculated visibility, determining a DOP forecast for GNSS signals observable in the cuboids at the time increments. The disclosed method also includes compressing the calculated DOP forecast spatially and temporally, and distributing the compressed DOP forecast via a content delivery network (CDN), responsive to queries from requestors to an API of the CDN, whereby the requestors' systems can take into account the DOP forecast for routing the vehicles or alerting the humans in the vehicles to a predicted navigation impairment.

Architecture for providing forecasts of GNSS obscuration and multipath

The technology disclosed relates to testing hybrid positioning systems, including systems that rely on MEMS sensors. In particular, it relates to methods and devices for configuring and running tests of hybrid positioning systems.

Hybrid location test system and method

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Carmarthen, United Kingdom

Colin Richard Ford