Spirent Communications, Inc.

Jeremy Charles Bennington

Paul Hansen

Esther Anyaegbu

Richard West

Matthew Pottle

Colin Richard Ford

Ronald Toh Ming Wong

Michael Colligan

Rafal Zbikowski

Arthur Edward Neeves

Raphael Grech

Adam Gleave

Dennis Berres

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.

GNSS forecast impacting receiver startup

At least three uses of the technology disclosed are immediately recognized. First, a video stream classifier can be trained that has multiple uses. Second, a trained video stream classifier can be applied to monitor a live network. It can be extended by the network provider to customer relations management or to controlling video bandwidth. Third, a trained video stream classifier can be used to infer bit rate switching of codecs used by video sources and content providers. Bit rate switching and resulting video quality scores can be used to balance network loads and to balance quality of experience for users, across video sources. Balancing based on bit rate switching and resulting video quality scores also can be used when resolving network contention.

Training an encrypted video stream network scoring system with non-reference video scores

Disclosed is a method of enhancing RTK position resolution using an RTK-enabled GNSS positioning receiver, including receiving an RTK base station signal for differential position calculation, and receiving a forecast assured navigation signal that includes data identifying line-of-sight availability of satellites generating GNSS signals at a position of the GNSS positioning receiver. Also included is excluding from, or reducing the weighting of, GNSS position calculation satellites not identified as line-of-sight available in the forecast assured navigation signal, and computing the GNSS position calculation combining the knowledge of line of sight, or not line of sight, satellites with the RTK base station signal to perform the differential position calculation and to determine an improved calculated position of the GNSS positioning receiver. Further included is unpacking from the forecast navigation signal receiver the data identifying line-of-sight availability of satellites based on an initially calculated location of the GNSS positioning receiver.

Enhancing RTK position resolution using an RTK-enabled GNSS positioning receiver

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 disclosed technology teaches training a NR VMOS score generator by generating synthetically impaired images from FR video using filters tuned to generate impaired versions and applying a FR VMOS generator to pairs of unimpaired FR images from the FR video and the impaired versions of the FR images to create ground truth scores for the impaired versions. The disclosed method also includes training by machine learning model an image evaluation classifier using the ground truth scores and the impaired versions to generate NR VMOS scores, and storing coefficients of the image evaluation classifier for use as the NR VMOS score generator. Also disclosed is generating a NR VMOS score by invoking the trained NR VMOS score generator, with stored coefficients generated by feeding the trained NR VMOS score generator with images captured from scenes in a video to be scored, and evaluating the images to generate NR VMOS scores.

Growing community of inventors

Greenwood, IN, United States of America

Jeremy Charles Bennington