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.

Embodiments including a method and apparatus for correction of a global navigation satellite system (GNSS) are described. In one example, the apparatus includes a communication interface and a processor. The communication interface is configured to a plurality of GNSS signals. The GNSS signals may include at least one almanac value and at least one ephemeris value. The processor is configured to generate a spatio-temporal graph model based on the at least one almanac value, the at least one ephemeris value, and a predetermined offset value for a base location. The spatio-temporal graph model analyzes subsequent GNSS signals to determined a predicted offset or a corrected GNSS position.

Embodiments including a method and apparatus for correction of a global navigation satellite system (GNSS) are described. In one example, the apparatus includes a communication interface and a processor. The communication interface is configured to a plurality of GNSS signals. The GNSS signals may include at least one almanac value and at least one ephemeris value. The processor is configured to generate a spatio-temporal graph model based on the at least one almanac value, the at least one ephemeris value, and a predetermined offset value for a base location. The spatio-temporal graph model analyzes subsequent GNSS signals to determined a predicted offset or a corrected GNSS position.

Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

A method, apparatus and computer readable storage medium are provided for determining whether or not GNSS navigation data are potentially manipulated. In the context of a method, GNSS reference data is received that represents one or more GNSS signals observed by a reference receiver at a reference position. The method also receives GNSS navigation data and determines, at least partially based on said GNSS reference data and the GNSS navigation data, a position estimate of said reference position. The method further includes determining, at least partially based on said reference position and the position estimate of said reference position, whether or not the GNSS navigation data are potentially manipulated.

A system is for determining a position on a golf course. The system has a master unit and at least one slave unit. The master unit and the at least one slave unit are adapted to communicate through a telecommunications network. The master unit comprises a receiver for a satellite navigation system, the receiver being operable at a fixed position on the golf course. The master unit is configured to: obtain a position determined by the receiver; process the displacement between the obtained position and the fixed position; and make the processed displacement available to the at least one slave unit through the telecommunications network. A slave unit then makes use of the processed displacement to improve positions determined by itself.

Some embodiments of the invention relate to generating correction information based on global or regional navigation satellite system (NSS) multiple-frequency signals observed at a network of reference stations, broadcasting the correction information, receiving the correction information at one or more monitoring stations, estimating ambiguities in the carrier phase of the NSS signals observed at the monitoring station(s) using the correction information received thereat, generating residuals, generating post-broadcast integrity information based thereon, and broadcasting the post-broadcast integrity information. Other embodiments relate to receiving and processing correction information and post-broadcast integrity information at NSS receivers or at devices which may have no NSS receiver, as well as to systems, NSS receivers, devices which may have no NSS receiver, processing centers, and computer programs. Some embodiments may for example be used for safety-critical applications such as highly-automated driving and autonomous driving.

Some embodiments of the invention relate to generating correction information based on global or regional navigation satellite system (NSS) multiple-frequency signals observed at a network of reference stations, broadcasting the correction information, receiving the correction information at one or more monitoring stations, estimating ambiguities in the carrier phase of the NSS signals observed at the monitoring station(s) using the correction information received thereat, generating residuals, generating post-broadcast integrity information based thereon, and broadcasting the post-broadcast integrity information. Other embodiments relate to receiving and processing correction information and post-broadcast integrity information at NSS receivers or at devices which may have no NSS receiver, as well as to systems, NSS receivers, devices which may have no NSS receiver, processing centers, and computer programs. Some embodiments may for example be used for safety-critical applications such as highly-automated driving and autonomous driving.

A device implementing a system for estimating device location includes at least one processor configured to receive a first estimated position of the device at a first time. The at least one processor is further configured to capture, using an image sensor of the device, images during a time period defined by the first time and a second time, and determine, based on the images, a second estimated position of the device, the second estimated position being relative to the first estimated position. The at least one processor is further configured to receive a third estimated position of the device at the second time, and estimate a location of the device based on the second estimated position and the third estimated position.

A device implementing a system for estimating device location includes at least one processor configured to receive a first estimated position of the device at a first time. The at least one processor is further configured to capture, using an image sensor of the device, images during a time period defined by the first time and a second time, and determine, based on the images, a second estimated position of the device, the second estimated position being relative to the first estimated position. The at least one processor is further configured to receive a third estimated position of the device at the second time, and estimate a location of the device based on the second estimated position and the third estimated position.