A method for applying GPS UAV attitude estimation to accelerate computer vision. The UAV has a plurality of GPS receivers mounted at fixed locations on the UAV. The method includes receiving GPS signals from each GPS satellite in view of the UAV, the GPS measurements comprising pseudo-range and carrier phase data representing the distance between each GPS receiver and each GPS satellite. Carrier phase and pseudo-range measurements are determined for each GPS receiver based on the pseudo-range and carrier phase data. The GPS carrier phase and pseudo-range measurements are compared pair-wise for each pair of GPS receiver and satellite. An attitude of the UAV is determined based on the relative distance measurements. A 3D camera pose rotation matrix is determined based on the attitude of the UAV. Computer vision image search computations are performed for analyzing the image data received from the UAV in real time using the 3D camera pose rotation matrix.

A method for applying GPS UAV attitude estimation to accelerate computer vision. The UAV has a plurality of GPS receivers mounted at fixed locations on the UAV. The method includes receiving GPS signals from each GPS satellite in view of the UAV, the GPS measurements comprising pseudo-range and carrier phase data representing the distance between each GPS receiver and each GPS satellite. Carrier phase and pseudo-range measurements are determined for each GPS receiver based on the pseudo-range and carrier phase data. The GPS carrier phase and pseudo-range measurements are compared pair-wise for each pair of GPS receiver and satellite. An attitude of the UAV is determined based on the relative distance measurements. A 3D camera pose rotation matrix is determined based on the attitude of the UAV. Computer vision image search computations are performed for analyzing the image data received from the UAV in real time using the 3D camera pose rotation matrix.

A method for determining a localization pose of an at least partially automated mobile platform, the mobile platform being equipped to generate ground images of an area surrounding the mobile platform, and being equipped to receive aerial images of the area surrounding the mobile platform from an aerial-image system. The method includes: providing a digital ground image of the area surrounding the mobile platform; receiving an aerial image of the area surrounding the mobile platform; generating the localization pose of the mobile platform with the aid of a trained convolutional neural network, which has a first trained encoder convolutional-neural-network part and a second trained encoder convolutional-neural-network part.

A method for determining a localization pose of an at least partially automated mobile platform, the mobile platform being equipped to generate ground images of an area surrounding the mobile platform, and being equipped to receive aerial images of the area surrounding the mobile platform from an aerial-image system. The method includes: providing a digital ground image of the area surrounding the mobile platform; receiving an aerial image of the area surrounding the mobile platform; generating the localization pose of the mobile platform with the aid of a trained convolutional neural network, which has a first trained encoder convolutional-neural-network part and a second trained encoder convolutional-neural-network part.

A Global Navigation Satellite System (GNSS) based navigation system with signal fault detection is provided. A least one controller is configured to; determine a true carrier phase measurement associated with each satellite signal received at each antenna of a plurality of spaced antennas; resolve integer ambiguities in true carrier phase measurement differences; and calculate at least one variable of a first navigation solution based on the obtained first set of resolved integer ambiguity measurements. The at least one controller is further configured to apply a solution separation process to repeatedly; calculate the at least one variable of a second navigation solution; determine a difference between the at least one variable of the second navigation solution and the first navigation solution; and detect a fault in satellite signals when the determined difference exceeds a defined threshold.

A distance measurement apparatus includes a calculation unit configured to calculate, based on phase information acquired by two distance measurement units at least one of which is movable, a distance between the two distance measurement units. One of the two distance measurement units includes an RSSI estimation unit configured to estimate, from respective three receiving signal intensities of three first carrier signals or respective three receiving signal intensities of three second carrier signals, the receiving signal intensity of a frequency having an average value, and a fading correction value calculation unit configured to calculate a fading correction value for the distance from the receiving signal intensity of a lowest frequency and the receiving signal intensity of a highest frequency. The calculation unit calculates the distance using a phase detection result obtained by receiving the three first carrier signals and the three second carrier signals and the fading correction value.

Disclosed is a system and method for receiving and processing a plurality of GNSS signals in a geo-location application to determine location, orientation and/or motion characteristics of a body on which the GNSS signal processing system is located. The system and method provide for precise synchronization of measurements of various signals and data associated with the GNSS signal processing system (including GPS systems), and provide for flexible configuration and allocation of resources used to receive and process GNSS signals to minimize power consumption and maximize efficiency and accuracy of the GNSS signal processing system. The flexibility of the system and method further provide for the scaling of one hardware system to address situations in which more or fewer antennas are employed, in which more or fewer data processing paths are needed, and in which the system is employed to determine various combinations of location, orientation, and motion characteristics.

Disclosed is a system and method for receiving and processing a plurality of GNSS signals in a geo-location application to determine location, orientation and/or motion characteristics of a body on which the GNSS signal processing system is located. The system and method provide for precise synchronization of measurements of various signals and data associated with the GNSS signal processing system (including GPS systems), and provide for flexible configuration and allocation of resources used to receive and process GNSS signals to minimize power consumption and maximize efficiency and accuracy of the GNSS signal processing system. The flexibility of the system and method further provide for the scaling of one hardware system to address situations in which more or fewer antennas are employed, in which more or fewer data processing paths are needed, and in which the system is employed to determine various combinations of location, orientation, and motion characteristics.

A method for determining a localization pose of an at least partially automated mobile platform, the mobile platform being equipped to generate ground images of an area surrounding the mobile platform, and being equipped to receive aerial images of the area surrounding the mobile platform from an aerial-image system. The method includes: providing a digital ground image of the area surrounding the mobile platform; receiving an aerial image of the area surrounding the mobile platform; generating the localization pose of the mobile platform with the aid of a trained convolutional neural network, which has a first trained encoder convolutional-neural-network part and a second trained encoder convolutional-neural-network part.

A method for determining a localization pose of an at least partially automated mobile platform, the mobile platform being equipped to generate ground images of an area surrounding the mobile platform, and being equipped to receive aerial images of the area surrounding the mobile platform from an aerial-image system. The method includes: providing a digital ground image of the area surrounding the mobile platform; receiving an aerial image of the area surrounding the mobile platform; generating the localization pose of the mobile platform with the aid of a trained convolutional neural network, which has a first trained encoder convolutional-neural-network part and a second trained encoder convolutional-neural-network part.