A system for determining a relative position associated to a first receiver and a second receiver includes a code module 402 configured to receive location data associated to the first receiver, receive location data associated to the second receiver, and determine a first estimate relative position associated to the first receiver and the second receiver; a float module 404 configured to determine a second estimate relative position associated to the first receiver and the second receiver at least partly from the first estimate relative position; a fix module 406 configured to determine a third estimate relative position associated to the first receiver and the second receiver at least partly from the second estimate relative position; and a sidereal module 408 configured to determine a fourth estimate relative position associated to the first receiver and the second receiver at least partly from the third estimate relative position.

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.

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.

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.

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.

A method can include receiving a set of satellite signals, refining the set of satellite signals to generate a refined set of satellite signals, determining a satellite solution for each satellite associated with a satellite signal in the refined set of satellite signals, applying an a-priori correction to the satellite signals, determining a set of time differenced satellite signals between the satellite signals from a current epoch and a previous epoch; and determining the positioning solution of the rover using a fusion engine that processes the differenced satellite signals and inertial measurement unit (IMU) data.

A method can include receiving a set of satellite signals, refining the set of satellite signals to generate a refined set of satellite signals, determining a satellite solution for each satellite associated with a satellite signal in the refined set of satellite signals, applying an a-priori correction to the satellite signals, determining a set of time differenced satellite signals between the satellite signals from a current epoch and a previous epoch; and determining the positioning solution of the rover using a fusion engine that processes the differenced satellite signals and inertial measurement unit (IMU) data.

An apparatus includes one or more processors and logic encoded in one or more non-transitory media for execution by the one or more processors and when executed operable to receive a request from a user for one or more services associated with satellites. The logic is further operable to while performing an initialization process, provide a graphical user interface (GUI) that includes information about one or more of the satellites. The logic is further operable to determine that the initialization process is complete. The logic is further operable to update the GUI to include an option to provide the one or more services associated with the satellites.

A system and method for detecting cyber-attacks. The method includes receiving satellite data from at least one satellite orbiting at a location of a ground-level sensor. The satellite data is received from the ground-level sensor. The method also includes determining whether the received satellite data is valid, and upon determining that the received satellite data is invalid, extracting a list of GNSS devices in a region where the ground-level sensor is deployed, and alerting each GNSS device in the list of GNSS devices on a potential cyber-attack.

An electronic device includes a GPS unit, a GPS information acquisition unit, a sensor information acquisition unit, and a reception condition determination unit. The GPS unit receives a radio wave from at least one of a plurality of positioning satellites. The GPS information acquisition unit acquires ephemeris information by the GPS unit and acquires satellite arrangement information of each of the plurality of positioning satellites acquiring the ephemeris information. The sensor information acquisition unit acquires geographical condition information of a current location at which the electronic device is present. The reception condition determination unit identifies the number of positioning satellites that the receiving unit can capture at the current location among the plurality of positioning satellites acquiring the ephemeris information based on the geographical condition information of the current location and the satellite arrangement information.