Disclosed is an obstacle avoiding method and apparatus for an unmanned aerial vehicle based on a multi-signal acquisition and route planning model. The method comprises: conducting signal acquisition processing on a first environmental area to obtain an initial millimeter-wave radar signal, an initial laser radar signal, an initial image signal and an initial ultrasonic signal; generating an initial three-dimensional environmental model according to a preset dynamic environment real-time modeling method; acquiring a motion parameter and a body shape parameter of the unmanned aerial vehicle and inputting the parameters into an initial route planning model corresponding to the initial three-dimensional environmental model based on a genetic algorithm to process to obtain an output of the initial route planning model; judging whether the output is capable of avoiding an obstacle; if yes, generating an obstacle avoiding flight instruction to require the unmanned aerial vehicle to fly through the first environmental area.

Examples disclosed herein relate to a sensor fusion system for use in an autonomous vehicle. The sensor fusion system has a radar detection unit with a metastructure antenna to direct a beamform in a field-of-view (“FoV”) of the vehicle, an analysis module to receive information about a detected object and determine control actions for the radar detection unit and the metastructure antenna based on the received information and on environmental information, and an autonomous control unit to control actions of the vehicle based on the received information and the environmental information.

Examples disclosed herein relate to a sensor fusion system for use in an autonomous vehicle. The sensor fusion system has a radar detection unit with a metastructure antenna to direct a beamform in a field-of-view (“FoV”) of the vehicle, an analysis module to receive information about a detected object and determine control actions for the radar detection unit and the metastructure antenna based on the received information and on environmental information, and an autonomous control unit to control actions of the vehicle based on the received information and the environmental information.

A system including an aircraft. A phased array may be configured to transmit electromagnetic (EM) energy toward rotor blades and receive EM energy in a direction from the rotor blades. A processor may be configured to: determine or obtain rotor blade information; determine or obtain aircraft information; based on the rotor blade information and the aircraft information, determine (a) a time to transmit EM energy or receive EM energy and (b) an angle to transmit EM energy or receive EM energy; and based on the rotor blade information and the aircraft information, control the phased array to adjust a beam pointing angle and to transmit EM energy for a duration at the beam pointing angle. The phased array may be configured to transmit EM energy for the duration at the beam pointing angle, wherein the transmitted EM energy is configured to reflect off a rotor blade toward a target.

A system for augmenting 360-degree aspect monostatic radar cross section of an aircraft. The system may comprise a pair of pods mountable on opposing wing tips of an aircraft and each having a pod housing with an elongate body tapering forwardly to a nose and rearwardly to a tail. Each pod may comprise a forward SDL disposed within the nose, a rear SDL disposed within the tail, and a pair of mid-body SDLs disposed within a mid-section of the pod housing. The SDLs may be arranged within the pods to reflect radiation and provide coverage around the aircraft over a region of about 360 azimuth degrees. Each SDL may comprise radar absorbing material located on an interior reflective surface, and portions of the elongate bodies may be constructed of radome material. The SDLs may be Luneburg lens having diameters of at least approximately 8-inches.

Examples disclosed herein relate to a sensor fusion system for use in an autonomous vehicle. The sensor fusion system has a radar detection unit with a metastructure antenna to direct a beamform in a field-of-view (“FoV”) of the vehicle, an analysis module to receive information about a detected object and determine control actions for the radar detection unit and the metastructure antenna based on the received information and on environmental information, and an autonomous control unit to control actions of the vehicle based on the received information and the environmental information.

Examples disclosed herein relate to a sensor fusion system for use in an autonomous vehicle. The sensor fusion system has a radar detection unit with a metastructure antenna to direct a beamform in a field-of-view (“FoV”) of the vehicle, an analysis module to receive information about a detected object and determine control actions for the radar detection unit and the metastructure antenna based on the received information and on environmental information, and an autonomous control unit to control actions of the vehicle based on the received information and the environmental information.

An apparatus includes a communications interface configured to receive, at a first aircraft, data related to a radiofrequency (RF) survey of a particular location. The RF survey is based on RF samples from at least a second aircraft. The apparatus further includes a radio, an onboard network system (ONS) configured to determine that the first aircraft is approaching the particular location, and a radio controller coupled to the radio. The radio controller is configured to automatically adjust, based on the RF survey and responsive to determining that the first aircraft is approaching the particular location, a parameter of the radio.

An apparatus includes a communications interface configured to receive, at a first aircraft, data related to a radiofrequency (RF) survey of a particular location. The RF survey is based on RF samples from at least a second aircraft. The apparatus further includes a radio, an onboard network system (ONS) configured to determine that the first aircraft is approaching the particular location, and a radio controller coupled to the radio. The radio controller is configured to automatically adjust, based on the RF survey and responsive to determining that the first aircraft is approaching the particular location, a parameter of the radio.

A system and method are disclosed for design of a suite of multispectral (MS) sensors and processing of enhanced data streams produced by the sensors for autonomous aircraft flight. The suite of MS sensors is specifically configured to produce data streams for processing by an autonomous aircraft object identification and positioning system processor. Multiple, diverse MS sensors image naturally occurring, or artificial features (towers buildings etc.) and produce data streams containing details which are routinely processed by the object identification and positioning system yet would be unrecognizable to a human pilot. The object identification and positioning system correlates MS sensor output with a-priori information stored onboard to determine position and trajectory of the autonomous aircraft. Once position and trajectory are known, the object identification and positioning system sends the data to the autonomous aircraft flight management system for autopilot control of the autonomous aircraft.