Systems and methods of non-line-of-sight passive detection and integrated early warning of an unmanned aerial system by a plurality of acoustic sensors are described. In some embodiments, the plurality of acoustic sensors is positioned within an intra-netted array in depth according to at least one of a terrain, terrain features, or man-made objects or structures. The acoustic sensors are capable of detecting and tracking unmanned aerial systems in non-line-of-sight environments. In some embodiments, the acoustic sensors may be in communication with internal electro-optical components or other external sensors, with orthogonal signal data then transmitted to remote observation stations for correlation, threat determination and if required, mitigation. The unmanned aerial systems may be classified by type and a threat level associated with the unmanned aerial system may be determined.

Systems and methods for detecting anomalies in aviation data communication systems (e.g., air traffic control surveillance systems), include a processor receiving device status information. A variational autoencoder receives and optimizes the device status information and determines whether it qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.

The present disclosure pertains to a system for determining a synthesized position of a vehicle. In some implementations, the system receives information related to the vehicle's location, the height above the ground surface of the terrain, and the vehicle's orientation from one or more first sensors; obtains a three dimensional topographical map of the terrain based on the location of the vehicle; and receives imagery data from one or more second sensors. The system determines a synthesized position of the vehicle relative to the terrain on the three dimensional topographical map by correlating a first pixel in the imagery data to a first vertex in the map, wherein the correlation is based on (i) pixel calibration information, the pixel calibration information comprising an angular relationship between the first pixel and the first vertex, (ii) the vehicle's height, and (iii) the vehicle's orientation.

The present disclosure pertains to a system for providing terrain imagery during low visibility conditions. In some implementations, the system receives information related to meteorological conditions for the environment around a vehicle; obtains a three dimensional topographical map of the terrain around the vehicle based on a location of the vehicle; and responsive to the information related to the meteorological conditions around the vehicle indicating low visibility conditions for a vehicle operator effectuates, based on the information related to imagery data in the output signals, a location of the vehicle, and the topographical map, presentation of one or both of simulated views of the terrain around the vehicle and a position of the vehicle on the three dimensional topographical map to the vehicle operator.

A method for managing avionic data between a flight management system FMS and one or more clients, the FMS comprising resources accessible through avionic services Ci (1,n); the execution of the Ci (1,n) determining an avionic functionality Fj (1,m); each of the Fj (1,m) associated with an intrusiveness parameter I.sub.k and a criticality parameter C.sub.k; the method comprises the steps of receiving a request specifying the call to an Fj (1,m); and determining a predefined execution right for a Ci (1,n), dependent on the predefined intrusiveness and/or criticality parameters associated with the Fj (1,m). Developments describe particularly the comparison of the execution rights, notice of a rejection, various avionic services and functionalities, the management of criticality ranges, consideration of the flight context, etc. Software and system aspects are described (e.g. equipment of EFB type).

The information processing device comprising an own aircraft information acquisition unit that acquires condition information regarding the condition of a target aircraft, a path acquisition unit that acquires a path that the target aircraft may follow, a related information acquisition unit that acquires related information regarding the path based on the condition information, and an output unit that outputs output information based on the related information, is capable of outputting information useful for flying aircraft.

Systems and methods for detecting anomalies in aviation data communication systems (e.g., air traffic control surveillance systems), include a processor receiving device status information. A variational autoencoder receives and optimizes the device status information and determines whether it qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.

An obstacle avoidance system for a stabilized aerial vehicle and a method of controlling same are provided. Using low angular resolution obstacle proximity data, such as from low angular resolution obstacle detection sensors, when a determination is made that an operator command to a vehicle propulsion system will result in a collision, the system overrides the operator command and substitutes an avoidance speed command and avoidance heading, while maintaining operator situational awareness, in a manner that is transparent to the operator. In an implementation, examination of objects requires the obstacle avoidance system to allow the vehicle to get close to obstacles. A human-portable aerial vehicle according to an implementation can be used for building surveillance, route inspection, surveillance of windows/hallways/rooftops, power distribution towers, pipelines, bridges, buildings or close examination of suspect objects.

A method is provided for monitoring a landing approach of an aircraft. The method includes receiving instrument landing system (ILS) signals; determining a glideslope deviation from the ILS signals; disabling, when the glideslope deviation is less than a first predetermined threshold, at least one glideslope alert function; evaluating a current glideslope condition by comparing a designated glideslope angle to a glideslope check value; and re-enabling the at least one glideslope alert function when the glideslope check value differs from the designated glideslope angle by more than a second predetermined threshold.

A method for heads-up control and interactive display of traffic targets via a heads-up display (HUD) includes: receiving position data from proximate aircraft (e.g., within a threshold range); arranging the aircraft into an ordered sequence based on proximity or other criteria; overlaying interactive symbology onto the HUD synthetic display corresponding to the proximate aircraft (including any aircraft within range but outside the HUD field of view, e.g., behind the aircraft or pilot); accepting control input from a user via a heads-up controller (e.g., manual control or voice command) and focusing a cursor on each proximate aircraft in sequence based on the control input; selecting, via the heads-up controller, focused aircraft via the heads-up controller; and designating, via the heads-up controller, selected aircraft for CDTI Assisted Visual Separation (CAVS) or other traffic applications.