A Precise Point Positioning (PPP) system is disclosed in which one or more Global Navigation Satellite System (GNSS) signals are obtained by a mobile device. The mobile device can obtain position information based on one or more position sources, where the position information is indicative of a location of the mobile device. One or more PPP positions of the mobile device can be determined based on the position information and the one or more GNSS signals, where a position uncertainty of the position information meets or is below an uncertainty threshold. A determination of whether at least one PPP position meets or is below one or more convergence thresholds can be made. In response to determining that at least one PPP position meets or is below the one or more convergence thresholds, the at least one PPP position can be provided.

Provided is a navigation board, a multi-source data fusion method for a navigation board and a transporter. The navigation board includes a printed circuit board, a global navigation satellite system module, an inertial sensor, a processor, and a data interface; the processor is configured to execute a large misalignment angle initialization algorithm, an inertial strapdown solution algorithm, and a multi-source data fusion solution; and a size of the navigation board is smaller than or equal to a size of a standard GNSS board, and the navigation board at least includes the same data interface as a data interface of the standard GNSS board.

A method and system for determining a heading of a vehicle. The method may include receiving, from a mobile computing device, a request for determining the heading of the vehicle. The method may also include retrieving, at least in response to the request, sensor data generated by a magnetometer of the mobile computing device within one or more first time slots, and obtaining a classifier trained to determine a predicted heading of the vehicle. The method may further include, for the sensor data of each of the one or more first time slots, obtaining a feature vector by extracting features from the sensor data, and determining, based on the obtained feature vector, a predicted heading by inputting the feature vector into the classifier. The method may also include determining the heading of the vehicle based on the obtained one or more predicted headings.

As a computing device travels on a trip from a starting location to a destination location, geographic coordinates are captured and stored. The geographic coordinates are tested against an obscuring condition to determine a portion of the geographic coordinates that satisfy the obscuring condition. Based on the determined portion of geographic coordinates satisfying the obscuring condition, altering the geographic coordinates in the determined portion of the geographic coordinates to reduce their precision or accuracy. The computing device transmits the altered geographic coordinates and the geographic coordinates that did not satisfy the obscuring condition.

A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

A system for estimating a receiver position with high integrity can include a reference station observation monitor configured to: receive a set of reference station observations associated with a set of reference stations, detect a predetermined event, and mitigate an effect of the predetermined event; a modeling engine configured to generate corrections; a reliability engine configured to validate the corrections; an observation monitor configured to: receive a set of satellite observations from a set of global navigation satellites corresponding to at least one satellite constellation; detect a predetermined event; and mitigate an effect of the predetermined event; a carrier phase determination module configured to determine a carrier phase ambiguity of the set of satellite observations; and a position filter configured to estimate a position of the receiver.

A method includes: receiving one or more positioning signals; determining that a UE is line-of-sight to fewer than a threshold number of positioning signal sources; determining a first position estimate hypothesis for the UE using a first position estimating process and one or more first measurements of the positioning signal(s); determining a second position estimate hypothesis for the UE using a second position estimating process and one or more second measurements of the positioning signal(s), wherein the second position estimating process uses a second parameter value of a parameter and the parameter is absent from the first position estimating process or has a first parameter value that is different from the second parameter value; and reporting a reported position estimate based on the first position estimate hypothesis or the second position estimate hypothesis in response to the UE being line-of-sight to fewer than the threshold number of positioning signal sources.

A method for control assistance of a vehicle. The method includes: receiving GNSS signals from at least one navigation satellite; ascertaining quality parameters of the GNSS signals, the quality parameters describing a reception quality of the received GNSS signals; and ascertaining a driving state of the vehicle based on the quality parameters of the GNSS signals by comparing values of the quality parameters of the received GNSS signals to previously known reference value clusters, the reference value clusters including a plurality of reference values for the particular quality parameters of the GNSS signals, each reference value cluster representing a previously known driving state, and each previously known driving state describing a state of the vehicle influencing a signal transmission of the GNSS signals; and providing a control assistance function based on the ascertained driving state.

Techniques for identifying the reliability of global navigation satellite system (GNSS) data include receiving data produced by one or more sensors during a trip within a vehicle, the data including GNSS sensor data and non-GNSS sensor data. Feature data is generated based at least in part on the GNSS sensor data and the non-GNSS sensor data. A reliability of at least a portion of the GNSS sensor data is determined by processing the feature data with a classifier.

A system and method, optionally implemented in a hardware circuitry, to receive a global positioning system pulse per second, GPS PPS, signal generated by a GPS receiver in a vehicle having a plurality of sensors; monitor the GPS PPS signal for an indication of a presence and a frequency of the GPS PPS signal within a predetermined threshold; generate a generated PPS signal synchronized with the GPS PPS signal; generate, based on an input of the generated PPS signal, a plurality of trigger signals, each of the generated plurality of trigger signals being selectively programmatically adjustable in at least one of a frequency and a phase, the selectively adjustability of each of the generated plurality of trigger signals being independent of the other generated plurality of trigger signals; and transmit at least one of the generated plurality of trigger signals to one or more of the sensors.