A system includes an automotive-based processor configured to receive a road-scanning instruction. The processor is further configured to instruct a vehicle-mounted sonar to scan an upcoming section of terrain and receive scan data from the sonar, responsive to the instruction. The processor is additionally configured to convert the scan data to a visual display showing at least obstacles and elevations and present the visual display on an in-vehicle display.

Various systems and methodologies may be utilized to determine whether a particular shipment/item is eligible for delivery between a manual delivery vehicle and a final destination location via an autonomous delivery vehicle. To ensure autonomous deliveries are performed in a resource effective manner, shipments/items deemed eligible for autonomous delivery may be vetted by comparing the destination for the autonomous delivery shipment/item against one or more manual delivery destinations (serviced by the manual delivery vehicle operator), and ultimately identifying an optimal launch location for the autonomous delivery vehicle to leave the manual delivery vehicle to complete the autonomous delivery. If the autonomous delivery location does not satisfy applicable autonomous delivery criteria, the autonomous delivery shipment/item may be reclassified for manual delivery by the manual delivery vehicle operator.

A multicopter includes: motors to respectively rotate three or more rotors; and a radar system to transmit and receive a signal wave and detect a target by using the signal wave. An object detection apparatus in the radar system transmits and receives a signal wave to perform a target detecting process. An antenna element is in a position to receive the transmission wave reflected off a rotor (a rotor-originated reflected wave). The signal wave received at the antenna element is inclusive of a target-originated reflected wave reflected off a target and a rotor-originated reflected wave. The apparatus determines whether or not a frequency band satisfying a predefined condition for identifying a frequency peak is contained in a frequency spectrum of the signal wave as received by the antenna element, and determines a peak of a frequency band satisfying the predefined condition to be a frequency of the target-originated reflected wave.

An agricultural system is disclosed comprising one or more radar sensors configured to acquire radar data representative of crop rows in an agricultural field. The system also comprises a controller configured to determine crop-property-data based on the radar data. The crop property data is representative of one or more properties of crop rows that are in a field.

Example foliage detection training systems and methods are described. In one implementation, a method receives data associated with a plurality of vehicle-mounted sensors and defines multiple regions of interest (ROIs) based on the received data. The method applies a label to each ROI, where the label classifies a type of foliage associated with the ROI. A foliage detection training system trains a machine learning algorithm based on the ROIs and associated labels.

A method of controlling an unmanned aerial vehicle executing a mission in a defined mission area including a first observation area within a Visual Line of Sight (VLOS) of a First Observer (FO), a second observation area within a VLOS of a second observer (SO), and a transition area within the VLOS of both the FO and the SO, the method including: the vehicle moving into the transition area after completing part of the mission within the first observation area, in sight of the FO; and in response to the vehicle moving into the transition area, determining whether the vehicle is in sight of the SO. The vehicle is including multiple processing systems in wireless communication with multiple remote user interfaces and a radar sensor mounted on the vehicle using a moveable mount for moving the radar sensor between different radar orientations, the radar sensor generating a range signal.

A drone is provided with a convolutional neural network that processes a fusion of video and radar data to identify and avoid collision threats. The drone includes a transmitter for transmitting the video data to a remote convolutional neural network for identifying structures and for further identifying threats or faults with the structures.

In an aircraft comprising a terrain warning device monitoring the flight of the aircraft with respect to the surrounding terrain and a terrain sensor, the terrain avoidance device is linked to the terrain warning device, to the terrain sensor and to a terrain relief database. The terrain avoidance device comprises a processing unit configured to determine a valid terrain avoidance trajectory as a function at one and the same time of an item of information regarding current position of the aircraft, of terrain relief information arising from the database and of terrain relief information provided by the terrain sensor when the terrain avoidance device receives a warning signal from the terrain warning device.

A system for detecting a target, the system comprises a transceiver and a signal processor; wherein the transceiver that is configured to: transmit a first pulse train that comprises multiple radio frequency (RF) pulses of a first non-linear polarity; receive first echoes resulting from the transmission of the first pulse train; generate first detection signals that represent the first echoes; and wherein the signal processor is configured to process the first detection signals to provide an estimated polarization orientation of a target; wherein the processing of the first detection signals comprises estimating a Jones matrix of the target.

Systems and method are provided for using millimeter-wave radar for terrain-aided navigation in support of autonomous guidance, landing, and mapping functions in all weather for unmanned air vehicles (UAVs). In an embodiment, a UAV can generate a map, based on millimeter-wave (MMW) radar returns, that rejects a large number of radar measurements as clutter and generates a flight path using waypoints based on the map. Embodiments of the present disclosure provide a means of correlating MMW radar returns with high resolution terrain maps to enable navigation in GPS-denied environments. This process significantly reduces cost, development time, and complexity when compared to conventional approaches.