Abstract: Described are systems and methods for generating training data that is used to train a machine learning system to detect moving objects represented in sensor data. The system and methods utilize position data received from a target vehicle to determine data points within sensor data that represents that target vehicle. For example, a station at a known location may receive Automatic Dependent Surveillance-Broadcast (“ADS-B”) data (position data) corresponding to a target vehicle that is within the field of view of a station sensor, such as a camera. The position data may then be correlated with the sensor data and projected into the sensor data to determine data points within the sensor data that represent the target vehicle. Those data points are then labeled to indicate the location, size, and/or shape of the target vehicle as represented in the sensor data, thereby producing training that may be provided to train a machine learning algorithm or system to detect moving objects, such as aircraft.

Abstract: Systems and methods related to identifying locations and/or ranges to objects using airborne imaging devices are disclosed. An object tracking system may include a plurality of aerial vehicles having associated imaging devices and a control station. Information related to positions and orientations of aerial vehicles and associated imaging devices may be received. In addition, imaging data may be received and processed to identify optical rays associated with objects within the imaging data. Further, a three-dimensional mapping of the identified optical rays may be generated, and locations or ranges of the objects relative to the aerial vehicles may be determined based on any intersections of optical rays within the three-dimensional mapping.

Abstract: An aerial vehicle may include a first sensor, such as a digital camera, having a lens or other component that includes a second sensor mounted thereto. Information or data, such as digital images, captured using the second sensor may be used to determine or predict motion of the lens, which may include components of translational and/or rotational motion. Once the motion of the lens has been determined or predicted, such motion may be used to stabilize information or data, such as digital images, captured using the first sensor, according to optical or digital stabilization techniques. Where operations of the first sensor and the second sensor are synchronized, motion of the second sensor may be modeled based on information or data captured thereby, and imputed to the first sensor.

Abstract: Systems and methods related to identifying locations and/or ranges to objects using airborne imaging devices are disclosed. An object tracking system may include a plurality of aerial vehicles having associated imaging devices and a control station. Information related to positions and orientations of aerial vehicles and associated imaging devices may be received. In addition, imaging data may be received and processed to identify optical rays associated with objects within the imaging data. Further, a three-dimensional mapping of the identified optical rays may be generated, and locations or ranges of the objects relative to the aerial vehicles may be determined based on any intersections of optical rays within the three-dimensional mapping.

Abstract: An aerial vehicle may include a first sensor, such as a digital camera, having a lens or other component that includes a second sensor mounted thereto. Information or data, such as digital images, captured using the second sensor may be used to determine or predict motion of the lens, which may include components of translational and/or rotational motion. Once the motion of the lens has been determined or predicted, such motion may be used to stabilize information or data, such as digital images, captured using the first sensor, according to optical or digital stabilization techniques. Where operations of the first sensor and the second sensor are synchronized, motion of the second sensor may be modeled based on information or data captured thereby, and imputed to the first sensor.

Abstract: A system may include first and second sensors configured to be coupled to a vehicle and generate surface sensor signals representative of a surface on which a location marker is disposed, and generate marker sensor signals representative of the location marker. The system may also include a sensor processor configured to estimate at least one of a position or an orientation of the first sensor relative to the surface on which the location marker is disposed based at least in part on the surface sensor signals, and estimate at least one of a position or an orientation of the second sensor relative to the location marker based at least in part on the marker sensor signals. The sensor processor may be configured to calculate at least one of the position or the orientation of the vehicle relative to the location marker based at least in part on the estimations.

Abstract: An aerial vehicle may include a first sensor, such as a digital camera, having a lens or other component that includes a second sensor mounted thereto. Information or data, such as digital images, captured using the second sensor may be used to determine or predict motion of the lens, which may include components of translational and/or rotational motion. Once the motion of the lens has been determined or predicted, such motion may be used to stabilize information or data, such as digital images, captured using the first sensor, according to optical or digital stabilization techniques. Where operations of the first sensor and the second sensor are synchronized, motion of the second sensor may be modeled based on information or data captured thereby, and imputed to the first sensor.

Abstract: An aerial vehicle may include a first sensor, such as a digital camera, having a lens or other component that includes a second sensor mounted thereto. Information or data, such as digital images, captured using the second sensor may be used to determine or predict motion of the lens, which may include components of translational and/or rotational motion. Once the motion of the lens has been determined or predicted, such motion may be used to stabilize information or data, such as digital images, captured using the first sensor, according to optical or digital stabilization techniques. Where operations of the first sensor and the second sensor are synchronized, motion of the second sensor may be modeled based on information or data captured thereby, and imputed to the first sensor.

Abstract: A system for automatically acquiring a target with a narrow field-of-view gimbaled imaging sensor. The system includes a target-detection subsystem including one or more target-detection imaging sensor with a first field-of-view, a target-tracking subsystem and a processing system in communication with the target-detection subsystem and the target-tracking imaging subsystem. The target-tracking subsystem includes a target-tracking imaging sensor with a second field-of-view smaller than the first field-of-view, and a gimbal mechanism for controlling a viewing direction of the target-tracking imaging sensor.

Abstract: A system for automatically acquiring a target with a narrow field-of-view gimbaled imaging sensor. The system includes a target-detection subsystem including one or more target-detection imaging sensor with a first field-of-view, a target-tracking subsystem and a processing system in communication with the target-detection subsystem and the target-tracking imaging subsystem. The target-tracking subsystem includes a target-tracking imaging sensor with a second field-of-view smaller than the first field-of-view, and a gimbal mechanism for controlling a viewing direction of the target-tracking imaging sensor.