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.

A method for preprocessing data for device operations can include preprocessing measurement data using a machine learning technique, determining, by a Kalman filter and based on (1) the preprocessed measurement data or the measurement data and (2) prediction data from a prediction model predicting a measurement associated with the measurement data, corrected measurement data, and providing the corrected measurement data based on the predicted measurement and the preprocessed measurement data.

A method for preprocessing data for device operations can include preprocessing measurement data using a machine learning technique, determining, by a Kalman filter and based on (1) the preprocessed measurement data or the measurement data and (2) prediction data from a prediction model predicting a measurement associated with the measurement data, corrected measurement data, and providing the corrected measurement data based on the predicted measurement and the preprocessed measurement data.

The disclosure relates to a method for GNSS-based localization of a vehicle, comprising at least the following steps: a) receiving GNSS-satellite signals from GNSS satellites and determining at least one item of distance information about the distance between the vehicle and the GNSS satellite emitting the relevant GNSS-satellite signal, b) determining at least one item of environmental information about the environment around the vehicle using image information determined using at least one environment sensor of the vehicle, which is capable of capturing images of at least part of the environment around the vehicle from different perspectives, c) determining at least one item of correction information using the at least one environmental information item, and d) correcting the at least one distance information item by means of the at least one correction information item.

The disclosure relates to a method for GNSS-based localization of a vehicle, comprising at least the following steps: a) receiving GNSS-satellite signals from GNSS satellites and determining at least one item of distance information about the distance between the vehicle and the GNSS satellite emitting the relevant GNSS-satellite signal, b) determining at least one item of environmental information about the environment around the vehicle using image information determined using at least one environment sensor of the vehicle, which is capable of capturing images of at least part of the environment around the vehicle from different perspectives, c) determining at least one item of correction information using the at least one environmental information item, and d) correcting the at least one distance information item by means of the at least one correction information item.

A tracking problem detection system for a machine may include tracking diagnostic circuitry including one or more tracking diagnostic processors configured to receive a location signal indicative of a location of a machine and a path signal indicative of a path location associated with at least a portion of a path for the machine to follow while maneuvering. The tracking diagnostic processors may also be configured to determine a tracking difference between the path location and the location of the machine, and determine a frequency of a signal associated with the tracking difference and/or a frequency of a signal associated with a yaw rate associated with the maneuvering. The tracking diagnostic processors may also be configured to detect, based at least in part on the frequencies of the signals associated with the tracking difference and/or the yaw rate, a tracking problem associated with maneuvering the machine.

A method, device, computer equipment and storage medium for GNSS time synchronization are disclosed. The method includes: receiving data packet of NMEA protocol, reading a valid UTC time from data packet of NMEA protocol, and storing the read valid UTC time in a time synchronization controller; receiving PPS signal, capturing a local time output by local clock when PPS signal is generated, and storing the local time in the time synchronization controller; reading a last local time stored before the current local time and reading the stored latest UTC time as a UTC time corresponding to the last local time when the time synchronization controller receives the current local time; and determining, by the time synchronization controller, a local time correction amount according to the last local time and the UTC time corresponding to the last local time, and correcting the local clock according to the local time correction amount.

An error model calibration method can analyze discrete distribution situations of a pseudo-range measurement error and a Doppler measurement error under different carrier-to-noise ratios and altitude helping to calibrate and improve the universality of error model calibration. Observation data is received and satellite data is acquired based on the observation data. A pseudo-range error array and a Doppler error array are calibrated based on the observation data, geometric parameters, and the satellite data. The pseudo range error array describes errors of two terminals under a carrier-to-noise ratio and altitude angle of a satellite. The Doppler error array describes a discrete distribution of the two terminals. The pseudo-range error models respectively corresponding to the at least two terminals are fit using the pseudo-range error array. The Doppler error models respectively corresponding to the at least two terminals are fit using the Doppler error array.

An error model calibration method can analyze discrete distribution situations of a pseudo-range measurement error and a Doppler measurement error under different carrier-to-noise ratios and altitude helping to calibrate and improve the universality of error model calibration. Observation data is received and satellite data is acquired based on the observation data. A pseudo-range error array and a Doppler error array are calibrated based on the observation data, geometric parameters, and the satellite data. The pseudo range error array describes errors of two terminals under a carrier-to-noise ratio and altitude angle of a satellite. The Doppler error array describes a discrete distribution of the two terminals. The pseudo-range error models respectively corresponding to the at least two terminals are fit using the pseudo-range error array. The Doppler error models respectively corresponding to the at least two terminals are fit using the Doppler error array.

A test device determines a Global Navigation Satellite System (GNSS) signal propagation delay in a GNSS signal distribution system (GSDS) for a radio access network. The test device includes a GNSS receiver and a clock. The GNSS receiver is connected at different times to a reference GSDS with a known signal propagation delay and to a device under test (DUT), including a second GSDS having an unknown signal propagation delay. One pulse per second (1PPS) signals are generated by the GNSS receiver and are compared to determine the unknown signal propagation delay of the DUT.