In some examples, systems and methods are described for output biasing maneuvers recommendations provided by at least one machine learning maneuver-recommendation (MLM) engine executing on an aerial vehicle. In some examples, output biasing data can be received that includes at least one risk tuning parameter that can influence which of the maneuver recommendations are selected by a maneuver decision engine executing on the aerial vehicle based on a maneuver confidence threshold for implementation by the aerial vehicle. The maneuver confidence threshold can be updated based on the at least one risk tuning parameter to provide an updated maneuver confidence threshold for the output biasing of the maneuvers recommendation provided by the at least one MLM engine. Vehicle command data for implementing a given maneuver recommendation can be outputted based on an evaluation of the updated maneuver confidence threshold.

A controller for an emergency response system, a computer program product for an emergency response system, and a method for facilitating rescue One embodiment may comprise a processor coupled to a memory, the memory containing program instructions. The program instructions, when executed on the processor, may cause the controller to collect waypoint data during a user's activity, detect a first trigger event indicating that the user needs assistance, determine a first response plan for an autonomous vehicle (AV) for the first trigger event based on the collected waypoint data, and execute the first response plan.

A system and method for reliably and efficiently monitoring and arbitrating the performance of one or more UTM infrastructure systems are provided herein. The method for monitoring and arbitrating a plurality of UTM infrastructure networks involves monitoring and arbitrating a plurality of unmanned traffic management (UTM) infrastructure networks comprising integrating a UTM arbitration system between the plurality of UTM infrastructure networks, wherein the UTM arbitration system is operably configured to simultaneously monitor the UTM infrastructure networks; monitoring information and/or data associated with one or more UTM systems associated with the UTM infrastructure networks; detecting the presence or absence thereof of one or more inconsistencies in the data and/or information associated with the one or more UTM systems; and initiating a reconciliation activity in response to detecting the presence of at least one inconsistency in the data and/or information associated with the one or more UTM systems.

A machine learning maneuver model can be programmed to generate maneuver data identifying a plurality of flight paths for maneuvering an aerial vehicle through an adverse weather condition and a flight path confidence score for each flight path of the plurality of flight paths based on at least weather sensor data characterizing the adverse weather condition. The flight path confidence score can be indicative of a probability of successfully maneuvering the aerial vehicle through the adverse weather condition according to a respective flight path. A maneuver decision engine can be programmed to evaluate each flight path confidence score for each flight path relative to a flight path confidence threshold to identify a given flight path of the plurality of flight paths through the adverse weather condition that poses a least amount of structural risk to the aerial vehicle.

A device receives a request for a flight path of UAV from a first location to a second location in a region, and determines, based on credentials associated with the UAV, whether the UAV is authenticated for utilizing the device and a network. The device determines, when the UAV is authenticated, capability information for the UAV based on the request and component information associated with the UAV. The device calculates the flight path from the first location to the second location based on the capability information and one or more of weather information, air traffic information, obstacle information, or regulatory information associated with the region. The device generates flight path instructions for the flight path based on one or more of the weather information, the air traffic information, the obstacle information, or the regulatory information associated with the region, and provides the flight path instructions to the UAV.

Embodiments for a terrain following (TF) radar configured for use in an airborne system are generally described herein. In some embodiments, a radar return comprising dual polarimetry radar data is processed to determine a Correlation Coefficient (CC), a Differential Reflectivity (ZDR), and a Specific Differential Phase (KDP). Discriminator logic is applied to the CC, the ZDR and the KDP to determine whether the radar return comprises solely rain. Further signal processing may be performed on the radar return when the radar return does not comprise solely rain. When the radar signal comprises solely rain, the radar return is tagged as a rain return. Applying the discriminator logic may include applying linear and/or quadratic functions to the CC, the ZDR and the KDP to determine whether the radar return comprises solely rain.

To bring an aircraft in flight to a runway, an automatic trajectory generation system obtains a procedure, called STARI procedure, which provides a final trajectory flyable by the aircraft to land on the runway, such that from the entry point of the final trajectory or from any point above it, a holding loop pattern of a predefined shape is flyable in order to dissipate energy if necessary. The automatic trajectory generation system then computes a lateral trajectory, avoiding any terrain relief, meteorological obstacles and military zones, between the current position of the aircraft and the entry point or a point above it, based on performance adapted to an operational state of the aircraft. An overall trajectory is thus obtained, by linking the computed lateral trajectory and the final trajectory of the STARI procedure, including iterations of the holding loop pattern if necessary.

A system and method for tilt dead reckoning is provided. The system and method allows an autopilot of an unmanned aerial vehicle (UAV) to perform dead reckoning with a hovering vehicle during GNSS signal loss by estimating the position and velocity of the vehicle based on its pitch and roll angles and known vehicle dynamics. The position and velocity are estimated using tables set up by a UAV integration engineer that provide the expected airspeed at given pitch and roll angles in steady state. This allows the UAV to attempt to follow waypoints when GNSS signal is lost without using any additional sensors.

A monitoring system for an aircraft has sensors that are used to sense the presence of objects around the aircraft for collision avoidance, navigation, or other purposes. At least one of the sensors may be configured to sense objects around the aircraft and provide data indicative of the sensed objects. The monitoring system may use information from the sensor and information about the aircraft to determine an escape envelope including possible routes that the aircraft can follow to avoid colliding with the object. The monitoring system may select an escape path based on the escape envelope and control the aircraft to follow the escape path to avoid collision with one or more objects.

According to one implementation, a flight support system of an aircraft includes storage and an information processing device. The storage stores position information on at least one river above which a space is an option for a flight path of the at least one aircraft. The information processing device automatically determines at least whether the flight path of the at least one aircraft should be over a specific river included in the at least one river in order to fly the at least one aircraft to a destination, based on the position information stored in the storage.