Abstract: An unambiguous heading direction is calculated to determine the forward/reverse state of a vehicle. A heading alignment error is determined at step 100, being the difference between a GNSS direction of motion and the unresolved IMU heading of the vehicle. The heading alignment error is adjusted by 180° to be within a predetermined range at step 200. The unresolved IMU heading of the vehicle 10 is adjusted using the heading alignment error to determine an ambiguous error corrected IMU heading at step 300. Step 400 determines whether the ambiguous error corrected IMU heading is substantially in the true direction of the nose of the vehicle. The unambiguous heading direction is calculated at step 500 by offsetting the ambiguous error corrected IMU heading by 180 degrees if the ambiguous error corrected IMU heading is substantially opposite the true direction of the nose the vehicle of the vehicle.

Abstract: A positioning method that calculates a lower accuracy positioning solution and applies an offset to the lower accuracy positioning solution to form a final positioning solution if a higher accuracy positioning solution is unavailable. The offset represents a difference between the lower accuracy positioning solution and the higher accuracy positioning solution at a point in time when the higher accuracy positioning solution was last available.

Abstract: Systems and methods for guiding a vehicle and vehicle sensor bias determination methods are disclosed. A method for guiding a vehicle includes a primary antenna of a primary survey-grade GNSS-receiver and a secondary antenna of a secondary GNSS-receiver mounted to the vehicle, which are at least temporarily receiving GNSS-signals of a global positioning system. A plurality of physical sensors mounted to the vehicle generate physical data indicative of respective measured physical parameters of at least part of the vehicle. The method includes de-biasing the physical data and applying a recursive statistical estimator, such as a Kalman filter, to the de-biased physical data and an output of the primary and secondary GNSS-receivers to determine a position and velocity of the vehicle.

Abstract: Systems and methods for guiding a vehicle and vehicle sensor bias determination methods are disclosed. A method for guiding a vehicle includes a primary antenna of a primary survey-grade GNSS-receiver and a secondary antenna of a secondary GNSS-receiver mounted to the vehicle, which are at least temporarily receiving GNSS-signals of a global positioning system. A plurality of physical sensors mounted to the vehicle generate physical data indicative of respective measured physical parameters of at least part of the vehicle. The method includes de-biasing the physical data and applying a recursive statistical estimator, such as a Kalman filter, to the de-biased physical data and an output of the primary and secondary GNSS-receivers to determine a position and velocity of the vehicle.

Abstract: A positioning method whereby inertial positioning data is calculated based upon measurements of an Inertial Navigation System. Virtual satellite ranging data is then generated based upon the inertial positioning data. The virtual satellite ranging data is then combined with received satellite ranging data from one or more satellites forming part of a Global Navigation Satellite System (GNSS). A GNSS positioning solution is then calculated based upon the combined received satellite ranging data and the virtual satellite ranging data.

Abstract: An unambiguous heading direction is calculated to determine the forward/reverse state of a vehicle. A heading alignment error is determined at step 100, being the difference between a GNSS direction of motion and the unresolved IMU heading of the vehicle. The heading alignment error is adjusted by 180° to be within a predetermined range at step 200. The unresolved IMU heading of the vehicle 10 is adjusted using the heading alignment error to determine an ambiguous error corrected IMU heading at step 300. Step 400 determines whether the ambiguous error corrected IMU heading is substantially in the true direction of the nose of the vehicle. The unambiguous heading direction is calculated at step 500 by offsetting the ambiguous error corrected IMU heading by 180 degrees if the ambiguous error corrected IMU heading is substantially opposite the true direction of the nose the vehicle of the vehicle.

Abstract: Systems and methods for determining a position of a vehicle are described. The system includes at least one GNSS sensor mounted to the vehicle for receiving GNSS signals of a global positioning system and at least one physical sensor mounted to the vehicle for generating physical data indicative of a physical parameter of at least a part of the vehicle. The system also includes a recursive statistical estimator, such as a Kalman Filter, in communication with the GNSS sensor(s) for seeding the recursive statistical estimator with an output of the GNSS sensor(s) to determine an estimated position of the vehicle. A data fusion module combines the estimated position and velocity of the vehicle with the physical data thus generating combined data, which is used to seed the recursive statistical estimator to determine an updated estimated position of the vehicle.

Abstract: A positioning method that calculates a lower accuracy positioning solution and applies an offset to the lower accuracy positioning solution to form a final positioning solution if a higher accuracy positioning solution is unavailable. The offset represents a difference between the lower accuracy positioning solution and the higher accuracy positioning solution at a point in time when the higher accuracy positioning solution was last available.

Abstract: Systems and methods for determining a position of a vehicle are described. The system includes at least one GNSS sensor mounted to the vehicle for receiving GNSS signals of a global positioning system and at least one physical sensor mounted to the vehicle for generating physical data indicative of a physical parameter of at least a part of the vehicle. The system also includes a recursive statistical estimator, such as a Kalman Filter, in communication with the GNSS sensor(s) for seeding the recursive statistical estimator with an output of the GNSS sensor(s) to determine an estimated position of the vehicle. A data fusion module combines the estimated position and velocity of the vehicle with the physical data thus generating combined data, which is used to seed the recursive statistical estimator to determine an updated estimated position of the vehicle.