Global navigation satellite systems and methods use L5 GNSS signals to acquire secondary code phases of those signals without using L1 GNSS signals to aid in the acquisition of secondary code phases. Various embodiments are described to perform this acquisition.
G01S 19/46 - Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
2.
METHODS AND SYSTEMS FOR COMPUTING GNSS TIME IN GNSS RECEIVERS
Methods and systems for processing GNSS signals to provide a computed fine time estimate of GNSS time. A method can include: receiving GNSS signals from GNSS SVs in a set of GNSS SVs; acquiring, from primary pseudorandom number (PRN) codes in the received GNSS signals, primary code phases for five (5) GNSS SVs, in the set of GNSS SVs, to derive pseudoranges to each of the five GNSS SVs; acquiring, from at least one secondary PRN code in the received GNSS signals, a secondary code phase of at least one GNSS SV, the acquired secondary code phase providing an estimated time data relative to an epoch boundary of the at least one secondary PRN code; and computing, with an equation based solver, an estimated GNSS time using the derived pseudoranges to each of the five GNSS SVs and the estimated time data derived from the acquired secondary code phase.
GNSS receivers and systems within such receivers use improvements to reduce memory usage while providing sufficient processing resources to receive and acquire and track ES band GNSS signals directly (without attempting in one embodiment to receive L1 GNSS signals). Other aspects are also described.
09 - Scientific and electric apparatus and instruments
39 - Transport, packaging, storage and travel services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software and software recorded on media and devices for providing positional accuracy of objects and locations using global navigation satellite system (GNSS) data; downloadable computer software for use in a receiver in connection with GNSS systems and for navigation and positioning applications; downloadable computer software and software recorded on media and devices for processing GNSS data used for accurate positioning of mobile and wearable electronic devices, internet of things (IoT) devices, vehicles, and other objects; downloadable computer software and software recorded on media and devices for controlling GNSS equipment and access to GNSS data and systems components; downloadable computer software and software recorded on media and devices for creating and executing computational algorithms in time, frequency and space domain to ensure access to GNSS systems; downloadable computer software and software recorded on media and devices for use in design and development of GNSS receivers and hardware, namely, Verilog and RTL embodiment of GNSS receiver engines and hardware; downloadable computer software and software recorded on media and devices for use in collecting, organizing, processing, transmitting and viewing data derived from global navigation satellite systems (GNSS) receivers and sensors for use in surveying, mapping, positioning, tracking and navigation; downloadable and recorded software development kit (SDK) comprised of software development tools and programming software for use in developing applications and programs for mobile electronic devices; integrated circuit modules and chipsets with and without software for providing location information using GNSS; global navigation satellite system based receivers and parts therefor Providing GNSS navigation services and data and information used in GNSS services, namely, providing positioning correction data for and to GNSS receivers and devices incorporating GNSS receivers; Providing GNSS navigation services and data and information used in GNSS services, namely, GNSS activity data gathering, mapping, tracking, recording, reporting and analysis services for use in connection with GNSS hardware and software; Providing GNSS navigation services and data and information used in GNSS services, namely, providing GNSS data and information used for the positioning of mobile and wearable electronic devices, internet of things (IoT) devices, vehicles, and other objects; Providing GNSS navigation services and data and information used in GNSS services, namely, providing positioning data as a service for others; Providing GNSS navigation services and data and information used in GNSS services, namely, providing GNSS data and information via GNSS, satellite, and global computer communication networks; GNSS navigation services; Providing GNSS navigation services and data and information used in GNSS services, namely, providing renderable map data and information, geographic location data and information, mobile device tracking data, vehicle and vessel tracking data and information for use in navigation and positioning Providing use of non-downloadable computer software for accessing, aggregating, optimizing, and transferring GNSS data for use in determining positional accuracy of mobile and wearable electronic devices, internet of things (IoT) devices, vehicles, other objects; providing use of non-downloadable computer software for purposes of determining positional and geographic accuracy; providing software-as-a-service (SaaS) services and platform-as-a-service (PaaS) computer software platforms, both for accessing, aggregating, optimizing, transferring, and distributing data for use in refining positional accuracy in the field of GNSS and positioning data; design and development of GNSS receivers and hardware, including Verilog and RTL embodiment of GNSS receiver engines and hardware; design and development of GNSS receivers, chipsets, and hardware; design and development of computer software in the field of GNSS data; providing technical advice and information relating to GNSS hardware, chipsets, software, and data for GNSS navigation; technical consulting services in connection with use of GNSS data for positional accuracy
5.
METHODS AND APPARATUSES FOR IMPROVED ACQUISITION OF MODERN GNSS SIGNALS
Many modem Global navigational satellite systems (GNSS) utilize signaling that includes a primary pseudorandom number (PRN) code and a secondary low rate PRN code, the latter of which is modulo-2 added to the first. In some GNSS acquisition receivers, the acquisition process performs a set of correlation operations that begin at a time that is not aligned with the epoch of the secondary PRN code resulting in loss of system performance. This disclosure provides new methods for dealing with such epoch misalignments and minimizes system performance loss. In one embodiment, a method combines a first and a second correlation operation, each using a different correlation epoch, to reduce the effect of epoch misalignments. This disclosure also provides methods -for advantageously processing a characteristic double frequency peak that may appear in. the acquisition process as a result of the correlation misalignment with the epoch of the secondary code.
G01S 19/31 - Acquisition or tracking of other signals for positioning
G01S 19/05 - Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing aiding data
Many modern Global navigational satellite systems (GNSS) utilize signaling that includes a primary pseudorandom number (PRN) code and a secondary low rate PRN code, the latter of which is modulo-2 added to the first. In some GNSS acquisition receivers in which precise a priori synchronization information is unavailable, the acquisition process performs a set of correlation operations that begin at a time that is not aligned with the epoch of the secondary PRN code. This epoch misalignment results in loss of system performance. This disclosure provides new methods for dealing with such epoch misalignments and minimizes system performance loss. In one embodiment, a method combines a first and a second correlation operation, each using a different correlation epoch, to reduce the effect of epoch misalignments. This disclosure also provides methods for advantageously processing a characteristic double frequency peak that may appear in the acquisition process as a result of the correlation misalignment with the epoch of the secondary code.
Global navigation satellite systems and methods use L5 GNSS signals to acquire secondary code phases of those signals without using L1 GNSS signals to aid in the acquisition of secondary code phases. Various embodiments are described to perform this acquisition.
G01S 19/46 - Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
8.
METHODS AND SYSTEMS FOR EXCESS PATH LENGTH CORRECTIONS FOR GNSS RECEIVERS
Methods and systems for estimating and using excess path length (EPL) corrections in GNSS receivers are described. A method can estimate the EPLs using a selection of line of sight and non line of sight pseudorange measurements, and these EPLs can be used to correct non selected non-line of sight pseudoranges. In one embodiment, a cloud based system can receive data from a crowd source set of EPL corrections (e.g., from GNSS receivers in an urban canyon environment) and then can develop a crowd sourced set of EPL corrections and then provide to GNSS receivers (some of which may be part of the crowd of GNSS receivers) the crowd sourced set of EPL corrections. The EPL corrections can be used to improve position solutions in, for example, an urban canyon.
Methods and systems for estimating and using excess path length (EPL) corrections in GNSS receivers are described. A method can estimate the EPLs using a selection of line of sight and non line of sight pseudorange measurements, and these EPLs can be used to correct non selected non-line of sight pseudoranges. In one embodiment, a cloud based system can receive data from a crowd source set of EPL corrections (e.g., from GNSS receivers in an urban canyon environment) and then can develop a crowd sourced set of EPL corrections and then provide to GNSS receivers (some of which may part of the crowd of GNSS receivers) the crowd sourced set of EPL corrections. The EPL corrections can be used to improve position solutions in, for example, an urban canyon.
G01S 19/41 - Differential correction, e.g. DGPS [differential GPS]
G01S 19/39 - Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
10.
MODERNIZED GLOBAL NAVIGATION SATELLITE SYSTEM (GNSS) RECEIVERS AND COMMERCIALLY VIABLE CONSUMER GRADE GNSS RECEIVERS
GNSS receivers and systems within such receivers use improvements to reduce memory usage while providing sufficient processing resources to receive and acquire and track E5 band GNSS signals directly (without attempting in one embodiment to receive L1 GNSS signals). Other aspects are also described.
09 - Scientific and electric apparatus and instruments
39 - Transport, packaging, storage and travel services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software and software recorded on media and devices for providing positional accuracy of objects and locations using global navigation satellite system (GNSS) data; downloadable computer software for use in a receiver in connection with GNSS systems and for navigation and positioning applications; downloadable computer software and software recorded on media and devices for processing GNSS data used for accurate positioning of mobile and wearable electronic devices, internet of things (IoT) devices, vehicles, and other objects; downloadable computer software and software recorded on media and devices for controlling GNSS equipment and access to GNSS data and systems components; downloadable computer software and software recorded on media and devices for creating and executing computational algorithms in time, frequency and space domain to ensure access to GNSS systems; downloadable computer software and software recorded on media and devices for use in design and development of GNSS receivers and hardware, namely, Verilog and RTL embodiment of GNSS receiver engines and hardware; downloadable computer software and software recorded on media and devices for use in collecting, organizing, processing, transmitting and viewing data derived from global navigation satellite systems (GNSS) receivers and sensors for use in surveying, mapping, positioning, tracking and navigation; downloadable and recorded software development kit (SDK) comprised of software development tools and programming software for use in developing applications and programs for mobile electronic devices; integrated circuit modules and chipsets with and without software for providing location information using GNSS; global navigation satellite system based receivers and parts therefor Providing GNSS navigation services and data and information used in GNSS services, namely, providing positioning correction data for and to GNSS receivers and devices incorporating GNSS receivers; Providing GNSS navigation services and data and information used in GNSS services, namely, GNSS activity data gathering, mapping, tracking, recording, reporting and analysis services for use in connection with GNSS hardware and software; Providing GNSS navigation services and data and information used in GNSS services, namely, providing GNSS data and information used for the positioning of mobile and wearable electronic devices, internet of things (IoT) devices, vehicles, and other objects; Providing GNSS navigation services and data and information used in GNSS services, namely, providing positioning data as a service for others; Providing GNSS navigation services and data and information used in GNSS services, namely, providing GNSS data and information via GNSS, satellite, and global computer communication networks; GNSS navigation services; Providing GNSS navigation services and data and information used in GNSS services, namely, providing renderable map data and information, geographic location data and information, mobile device tracking data, vehicle and vessel tracking data and information for use in navigation and positioning Providing use of non-downloadable computer software for accessing, aggregating, optimizing, and transferring GNSS data for use in determining positional accuracy of mobile and wearable electronic devices, internet of things (IoT) devices, vehicles, other objects; providing use of non-downloadable computer software for purposes of determining positional and geographic accuracy; providing software-as-a-service (SaaS) services and platform-as-a-service (PaaS) computer software platforms, both for accessing, aggregating, optimizing, transferring, and distributing data for use in refining positional accuracy in the field of GNSS and positioning data; design and development of GNSS receivers and hardware, including Verilog and RTL embodiment of GNSS receiver engines and hardware; design and development of GNSS receivers, chipsets, and hardware; design and development of computer software in the field of GNSS data; providing technical advice and information relating to GNSS hardware, chipsets, software, and data for GNSS navigation; technical consulting services in connection with use of GNSS data for positional accuracy
12.
METHODS AND SYSTEMS FOR ENHANCED RANSAC SELECTION OF GNSS SIGNALS
Machine learning techniques are used to perform RANSAC like processing in a GNSS receiver. A model (e.g., one or more neural networks) is trained to perform this processing to generate a selection of a subset of GNSS SVs. In one embodiment, the trained model is used during inferencing in a GNSS receiver. A method in a GNSS receiver can include the following operations: receiving GNSS signals from a plurality of SVs; extracting a set of features from the received GNSS signals, the set of features being predetermined based on a trained model in the GNSS receiver, the trained model having been trained to select a subset of GNSS SVs based on the set of features including RANSAC (random sample consensus) residuals; applying the set of features as an input to the trained model; generating, by the trained model, a selection of a subset of GNSS SVs based in part on RANSAC residuals and based on other features in the set of features; and computing a position solution using GNSS signals received from the subset of GNSS SVs.
A method in a GNSS receiver can include the following operations: receiving GNSS signals from a plurality of SVs; extracting a set of features from the received GNSS signals, the set of features being predetermined based on a trained model in the GNSS receiver, the trained model having been trained to select a subset of GNSS SVs based on the set of features including RANSAC residuals; applying the set of features as an input to the trained model; generating, by the trained model, a selection of a subset of GNSS SVs based in part on RANSAC residuals and based on other features in the set of features; and computing a position solution using GNSS signals received from the subset of GNSS SVs.
G01S 19/39 - Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
GNSS receivers and systems within such receivers use improvements to reduce memory usage while providing sufficient processing resources to receive and acquire and track E5 band GNSS signals directly (without attempting in one embodiment to receive L1 GNSS signals). Other aspects are also described.
GNSS receivers and systems within such receivers use improvements to reduce memory usage while providing sufficient processing resources to receive and acquire and track E5 band GNSS signals directly (without attempting in one embodiment to receive L1 GNSS signals). Other aspects are also described.
Machine learning techniques are used to compute predicted range rate errors in a GNSS receiver. In one embodiment, training data is computed to provide true range rate error data for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the true range rate error data to train a model (e.g., a set of one or more neural networks) that can produce predicted range rate errors for use in correcting measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct Doppler measurements using the predicted range rate errors provided by the trained set of one or more neural networks.
Machine learning techniques are used, in one embodiment, to mitigate multipath in an L5 GNSS receiver. In one embodiment, training data is generated to provide ground truth data for excess path length (EPL) corrections for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the ground truth data to train a set of one or more neural networks that can produce EPL corrections for pseudorange measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct pseudorange measurements using EPL corrections provided by the trained set of neural networks.
Machine learning techniques can be used to mitigate multipath in a GNSS receiver that includes a first trained model that provides extra path length (EPL) corrections in the GNSS receiver. The first trained model can be updated using an updated and trained model from one or more assistance servers that are in communication with the GNSS receiver. The GNSS receiver can provide, for a particular computed position and time, extracted features from received GNSS signals to the one or more assistance servers. The assistance servers can then use the extracted features and a source of true EPL corrections (e.g., from a 3D building map database for the particular computed position and time) to train a server model. The server model, once trained to a desired level of accuracy, can be transmitted to the GNSS receiver to replace the first trained model. The server model can be compared to the first trained model to verify it can provide more accurate EPL corrections than the first trained model. The server model and the source of true EPL corrections can be specific for a geographic region, so different regions have different server models based on the corresponding sources of true EPL corrections.
G01S 19/23 - Testing, monitoring, correcting or calibrating of a receiver element
G01S 19/05 - Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing aiding data
Machine learning techniques to mitigate multipath in a GNSS receiver that includes a first trained model that provides extra path length (EPL) corrections in the GNSS receiver. The first trained model can be updated using an updated and trained model from one or more assistance servers that are in communication with the GNSS receiver. The GNSS receiver can provide, for a particular computed positio and time, extracted features from received GNSS signals to the one or more assistance servers. The assistance servers can then use th extracted features and a source of true EPL corrections to train a server model. The server model can be transmitted to the GNSS receiver to replace the first trained model. The server model and the source of true EPL corrections can be specific for a geographic region, so different regions have different server models based on the corresponding sources of true EPL corrections..
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
Machine learning techniques are used, in one embodiment, to mitigate multipath in an L5 GNSS receiver. In one embodiment, training data is generated to provide ground truth data for excess path length (EPL) corrections for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the ground truth data to train a set of one or more neural networks that can produce EPL corrections for pseudorange measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct pseudorange measurements using EPL corrections provided by the trained set of neural networks.
G01S 19/49 - Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
21.
MACHINE LEARNING IN GNSS RECEIVERS FOR IMPROVED VELOCITY OUTPUTS
Machine learning techniques are used to compute predicted range rate errors in a GNSS receiver. In one embodiment, training data is computed to provide true range rate error data for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the true range rate error data to train a model (e.g., a set of one or more neural networks) that can produce predicted range rate errors for use in correcting measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct Doppler measurements using the predicted range rate errors provided by the trained set of one or more neural networks.
G01S 19/27 - Acquisition or tracking of signals transmitted by the system creating, predicting or correcting ephemeris or almanac data within the receiver
G01S 19/20 - Integrity monitoring, fault detection or fault isolation of space segment
GNSS receivers and systems within such receivers use improvements to reduce memory usage while providing sufficient processing resources to receive and acquire and track E5 band GNSS signals directly (without attempting in one embodiment to receive L1 GNSS signals). Other aspects are also described.
Global navigation satellite systems and methods use L5 GNSS signals to acquire secondary code phases of those signals without using L1 GNSS signals to aid in the acquisition of secondary code phases. Various embodiments are described to perform this acquisition.
Global navigation satellite systems and methods use L5 GNSS signals to acquire secondary code phases of those signals without using L1 GNSS signals to aid in the acquisition of secondary code phases. Various embodiments are described to perform this acquisition.
G01S 19/46 - Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
This disclosure describes methods, systems and machine readable media that can provide position solutions using, for example, pattern matching with GNSS signals in urban canyons. In one method, based upon an approximate location in an urban canyon and a set of 3D data about building structures in the urban canyon, an expected signal reception data can be generated for both line of sight and non-line of sight GNSS signals from GNSS satellites, or other sources of GNSS signals, at each point in a set of points in a grid (or other model) in the vicinity of the approximate location). This expected signal reception data can be matched to a received set of GNSS signals that have been received by a GNSS receiver, and the result of the matching can produce an adjustment to the approximate location that is used in the position solution of the GNSS receiver.
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
G01S 19/32 - Multimode operation in a single same satellite system, e.g. GPS L1/L2
G01S 19/40 - Correcting position, velocity or attitude
This disclosure describes methods, systems and machine readable media that can provide position solutions using, for example, pattern matching with GNSS signals in urban canyons. In one method, based upon an approximate location in an urban canyon and a set of 3D data about building structures in the urban canyon, an expected signal reception data can be generated for both line of sight and non-line of sight GNSS signals from GNSS satellites, or other sources of GNSS signals, at each point in a set of points in a grid (or other model) in the vicinity of the approximate location). This expected signal reception data can be matched to a received set of GNSS signals that have been received by a GNSS receiver, and the result of the matching can produce an adjustment to the approximate location that is used in the position solution of the GNSS receiver.
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
G01C 21/26 - Navigation; Navigational instruments not provided for in groups specially adapted for navigation in a road network
G01S 19/08 - Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing integrity information, e.g. health of satellites or quality of ephemeris data
27.
MODERNIZED GLOBAL NAVIGATION SATELLITE SYSTEM RECEIVERS
GNSS receivers and systems within such receivers use improvements to reduce memory usage while providing sufficient processing resources to receive and acquire and track E5 band GNSS signals directly (without attempting in one embodiment to receive L1 GNSS signals). Other aspects are also described.
