371 results about "Flight speed" patented technology

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle positioning method and system combining environment characteristics. According to the technical scheme, the method comprises the steps that multi-modal data of the environment where the unmanned aerial vehicle is located are collected through a multi-source heterogeneous sensor, and the multi-modal data comprise GNSS signal data, visual image data, laser radar point cloud data, inertia measurement data and electromagnetic interference monitoring data; and carrying out cooperative processing on the multi-modal data by utilizing the fusion deep learning model. A comprehensive basis is provided for subsequent processing through multi-source data collection, data quality and feature matching reliability are improved through fusion processing, timely and accurate positioning is guaranteed through real-time calculation, safe and efficient flight is achieved through a decision-making model based on accurate positioning, and the flight speed is high. The method not only solves the traditional technical problems of signal shielding, interference, dynamic interferent influence and contradiction between real-time performance and precision in a complex environment, but also shows excellent positioning stability and adaptability in various complex scenes.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle dynamic obstacle avoidance method based on multi-sensor fusion. Comprising the steps of receiving an image sequence, a depth point cloud set, a pose state parameter and a flight speed parameter; calculating an illumination distortion gradient value and a speckle noise entropy value based on the image sequence and the depth point cloud set; performing coordinate registration and feature extraction on the image sequence, the depth point cloud set and the pose state parameter, and outputting a visual feature set and a radar feature set; generating a weighted visual feature set and a weighted radar feature set through a weight adjustment function, and generating a joint heterogeneous feature tensor through alignment splicing; and generating a body attitude and thrust control instruction by using the combined heterogeneous feature tensor through a probability prediction model and a model prediction control algorithm device. According to the method, through cross-domain multiplexing and deep coupling of the body physical parameters in the data stream, error accumulation and decision delay caused by multi-stage series calculation are avoided, and global performability of an obstacle avoidance task is facilitated.

The invention discloses an unmanned aerial vehicle cluster relay system optimization method, and aims to significantly reduce the overall outage probability of the system by performing adaptive optimization on cooperative position deployment and flight trajectories of multiple unmanned aerial vehicles under multiple actual constraint conditions of flight speed, safety distance, flight boundary and the like. Therefore, the reliability and the service quality of the multi-unmanned aerial vehicle relay communication network are improved. According to the method, a Markov decision process (MDP) model is constructed, a multi-unmanned aerial vehicle trajectory optimization problem is converted into a multi-agent reinforcement learning problem, and efficient optimization of unmanned aerial vehicle trajectories is realized by using a centralized training-distributed execution framework, so that the outage probability of a communication system is minimized.

The invention provides a path planning and energy management cooperative regulation and control method for a multi-agent unmanned aerial vehicle, and the method comprises the following steps: building a kinetic model based on a six-degree-of-freedom motion equation of a four-rotor unmanned aerial vehicle, and building an energy consumption model through combining the power of a motor and the discharge characteristics of a battery; sensing the state of the unmanned aerial vehicle in real time, defining a state space and an action space of a deep reinforcement learning algorithm, and generating navigation points by using the deep reinforcement learning algorithm; receiving a delivery request, and dynamically allocating tasks according to the state of the unmanned aerial vehicle by adopting a deep reinforcement learning algorithm; performing path planning on the unmanned aerial vehicle for task allocation by adopting an A * algorithm according to the navigation points in combination with the multi-dimensional cost, and obtaining an optimal path; dispersing the optimal path into a speed instruction, and dynamically adjusting the course angle and the flight speed of the unmanned aerial vehicle through a PID controller; through combination of deep reinforcement learning and an A * algorithm, the problems of path planning and energy management of the multi-agent quad-rotor unmanned aerial vehicle logistics distribution system in complex terrains are effectively solved.

The invention discloses a tilt rotor aircraft flight control method in a tilt transition stage, and the method comprises the steps: determining a reverse tilt state point which is a waypoint of which the to-be-fly distance to the starting point of a multi-rotor mode descent stage is greater than or equal to the shortest design deceleration distance and the height is H; and when the tilt rotor aircraft is located at the reverse tilt state point, controlling the tiltable rotors to tilt reversely according to the tilt angle instruction, and controlling the tilt rotor aircraft to fly forward according to the reverse tilt transition flight speed. In the reverse tilting transition stage, the tilting angle rate is calculated based on the to-be-fly distance, and corresponding flight control strategies are executed for different stages in the reverse tilting transition stage in combination with comparison of the to-be-fly distance and the forward distance threshold value and the tilting angle range, so that accurate control over the position of the aircraft in the reverse tilting transition stage is achieved.

The invention relates to a tilt rotorcraft semi-physical simulation system and method, and relates to the technical field of wind tunnel flight test semi-physical simulation. Based on the initialized system with the test parameters set, six-component balance load, wind speed and attitude data are collected in real time, and the collected key physical data are filtered and then sent to the flight simulation subsystem; the flight simulation subsystem carries out calculation based on a flight dynamics model, a rotor wing power model, a control instruction preprocessing model and the received data and outputs an actuation instruction; the actuator receives an actuation instruction and drives the aircraft model, and the airborne sensor feeds back new attitude data to form a control closed loop; the flight simulation subsystem sends state data to the 3D visual simulation module in real time to realize synchronization of control and visual; in this way, the stable state under the combination of different tilting angles and flight speeds is recorded, and a tilting transition corridor is obtained through fitting. Therefore, simulation precision, test safety and iteration efficiency are taken into consideration, and fused semi-physical simulation is formed.

The invention relates to the technical field of trailing vortex dynamic monitoring, in particular to a short-distance parallel runway wake flow dynamic prediction method based on deep learning, which comprises the following steps of: acquiring disturbance energy level and coordinate direction and fitting propagation state, extracting disturbance source features and combining with spatial direction constraint, fitting boundaries according to propagation included angle and speed change rate and classifying, and obtaining a dynamic prediction result. Acquiring a track coordinate course angle and a flight speed, calculating a track vector and a boundary normal included angle, analyzing an interference risk factor in combination with a ratio of the speed to a propagation direction, dividing disturbance levels, and outputting a preliminary prediction result. According to the method, the state vector and the disturbance data cooperatively extract features and constrain the direction, the disturbance source positioning precision is improved, the boundary is described by combining the speed change rate and the propagation included angle, the recognition robustness is enhanced, the risk level is evaluated by fusing multi-dimensional parameters, the disturbance intensity is dynamically corrected, the track deviation is introduced, and the prediction accuracy and the response efficiency are improved.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method and system based on laser ranging, and the method comprises the steps: generating point cloud data for the flight environment of an unmanned aerial vehicle in real time, obtaining the distance between each point cloud and the unmanned aerial vehicle at each moment in each control period, and determining the distance between each point cloud and the unmanned aerial vehicle; the point cloud resolution and the point cloud signal-to-noise ratio of each moment, and the flight speed of the unmanned aerial vehicle at each moment; calculating the environment complexity of each moment; calculating a flight margin of each moment to obtain a flight risk value of each moment; determining an attenuation evaluation value at each moment, and determining an environment interference degree at each moment; and the adjustment coefficient of each moment is obtained, the step length of the fast expansion random tree algorithm corresponding to the next control period is adjusted, and the flight path of the unmanned aerial vehicle is planned by using the fast expansion random tree algorithm. By dynamically adjusting the step length of the fast expansion random tree algorithm, efficient and safe path planning can be realized in a complex and dynamic flight environment.

The invention discloses an unmanned aerial vehicle autonomous route planning method and system capable of adapting to environmental changes. The method comprises the steps of obtaining a dynamic three-dimensional situation map of a flight area of an unmanned aerial vehicle based on multi-source data of the flight area of the unmanned aerial vehicle; acquiring a current no-fly zone and weather forecast information of the flight area of the unmanned aerial vehicle, and analyzing and acquiring a flight risk distribution condition of the flight area of the unmanned aerial vehicle based on the dynamic three-dimensional situation map; considering the flight risk distribution condition of the unmanned aerial vehicle flight area, the unmanned aerial vehicle flight task and the unmanned aerial vehicle self-state information to generate an unmanned aerial vehicle reference route; and automatically adjusting the flight speed, the flight height and the flight course of the unmanned aerial vehicle according to a preset flight parameter adjustment model based on the acquired multi-source data and the state information of the unmanned aerial vehicle in the process of flying along the reference route. According to the invention, the reliability and safety of flight task execution of the unmanned aerial vehicle in a complex dynamic environment can be improved.

The present application relates to a kind of unmanned aerial vehicle control method considering weather risk, including the following steps: real-time acquisition unmanned aerial vehicle flight environment data, synchronous receipt cloud layer evolution map and ground radar's wind shear early warning signal, process to obtain multimodal meteorological data;Predict the storm intensity distribution in future 2-5 minutes track area, and generate three-dimensional threat level grid map;For conventional meteorological risk, build threat level-avoidance strategy mapping table, judge threat level, and call corresponding avoidance strategy, guide unmanned aerial vehicle to execute corresponding avoidance action;When encountering extreme situation, activate anti-interference navigation mode, enhance flight stability by rotating speed-pitch angle collaborative control, trigger emergency protocol of alternate landing;After completing avoidance action, re-plan flight path in combination with space-time constraint, reasonably adjust flight speed.The present application also relates to a kind of unmanned aerial vehicle control system considering weather risk.The present application can improve the flight safety and stability of unmanned aerial vehicle under complex weather conditions.

The invention relates to the field of RCS testing, in particular to a dynamic RCS testing system and method based on an unmanned aerial vehicle. The system comprises an unmanned aerial vehicle platform subsystem, a ground measurement radar subsystem and a comprehensive control and data processing subsystem. The unmanned aerial vehicle platform subsystem is composed of an unmanned aerial vehicle carrier, a target installation mechanism and a first differential GNSS module. The first differential GNSS module is used for acquiring space three-dimensional coordinates, flight speed and course of the unmanned aerial vehicle and a target; the ground measurement radar subsystem is composed of an RCS measurement radar, a transmitting antenna, a receiving antenna and a second differential GNSS module; the ground measurement radar subsystem is composed of an RCS measurement radar, a transmitting antenna, a receiving antenna and a second differential GNSS module. The comprehensive control and data processing subsystem is composed of a flight control unit, a radar control unit, a data synchronization unit and a data processing and inversion unit. The method is suitable for the RCS test.

The invention belongs to the technical field of unmanned aerial vehicle line simulation, and provides a power transmission line unmanned aerial vehicle line simulation flight navigation control method and system. The method comprises the following steps: pre-planning a reference flight path according to a power transmission line coordinate of a to-be-simulated line of an unmanned aerial vehicle; on the reference flight path, determining a detection frame coordinate center position of the power transmission line according to the infrared image and the visible light image of the power transmission line, and dynamically predicting the position of the unmanned aerial vehicle according to a change trend of the detection frame coordinate center position of the power transmission line and a slope change trend of a fitting straight line of the power transmission line; extracting temperature gradient field characteristics in a detection frame of the power transmission line, calculating a main direction angle of the power transmission line according to the temperature gradient field characteristics, and calculating a direction angle of the power transmission line by combining with a slope of a fitting straight line of the power transmission line; and controlling the flight speed on each coordinate axis of the unmanned aerial vehicle coordinate system according to the direction angle of the power transmission line, the dynamically predicted unmanned aerial vehicle position and the preset flight speed.

The application is suitable for the field of flying cars, and provides a vertical take-off and landing flying car, which comprises a car body, a main lift assembly arranged on the car body and placed inside the car body, a folding wing assembly arranged on the car body and placed on the top of the car body, and a back thrust power assembly arranged on the car body and placed on the top of the tail end of the car body. The application aims to solve the technical problems in the prior art that one kind of flying car is vertically taken off and landed, but does not have a fixed-wing flight mode, provides lift by a rotor, is more like a variant of a helicopter, has a small flight speed, a short flight range, and a complex control; another kind of flying car is taken off by sliding, has a main wing for generating lift, but has a long sliding distance for taking off and landing, has a high requirement for a landing site, and greatly limits the use scene.

The invention relates to an aircraft conflict detection method based on high-precision data. The method comprises the following steps: firstly, acquiring high-precision operation data such as real-time three-dimensional coordinates and flight speed of an aircraft; the overlapping risk of the spatial dimension is identified through a three-step method including line segment intersection judgment, height difference comparison and actual spatial distance accounting; the time dimension is combined with the flight speed, the course deviation correction coefficient and the airspace environment resistance coefficient to calculate the estimated time for reaching the potential conflict point, and early warning is triggered through a time difference threshold value; according to the method, a space-time detection result is fused to generate a conflict level, then a calculation priority is adjusted based on a computing power optimization strategy, a conflict avoidance instruction is output, and the method aims at solving the problems that a traditional method in a million-level aircraft data scene is large in calculation amount and insufficient in computing power, and existing airspace gridding detection is poor in dynamic trajectory adaptability and low in precision.

The invention relates to the technical field of aircraft route planning, in particular to an aircraft control method and system for preventing long-distance flight collision, and the method comprises the steps: obtaining the offset distance between an aircraft and a set route at each moment in the flight process, and the measurement distance between the aircraft and a nearest obstacle; obtaining the dynamic stability of the aircraft in each time interval; determining the long-term balance degree of the aircraft in each time interval based on the autocorrelation of the flight heights of the aircraft at all moments in each time interval and the linear growth trend of the distance between the aircraft and the starting point in each time interval in combination with the fluctuation condition of extreme value distribution in the flight speeds of the aircraft at all moments in the time interval; and correcting a Q value updating method in the depth Q network, and performing route planning on the aircraft by using the corrected depth Q network. Therefore, the long-distance flight collision risk of the aircraft is reduced.

The utility model discloses an integrated aileron driving system, which belongs to the field of aircraft control systems and comprises a fixed wing, an aileron is arranged on one side of the fixed wing, the aileron is rotatably connected with the fixed wing through a first linkage shaft, the upper surface of the aileron is rigidly connected with a first speed reducing plate, and a second speed reducing plate is arranged on the other side of the fixed wing. A power rotating assembly is arranged on the upper surface of the fixed wing, the power rotating assembly is connected with the upper surface of the first air brake and used for driving the first air brake to move, fixed limiting pieces are symmetrically arranged at the two ends of the surface of the side, close to the aileron, of the fixed wing, and a second air brake is arranged between the fixed limiting pieces. The second air brake is located on the lower surface of the aileron, and the first air brake and the second air brake are connected through a transmission assembly. According to the integrated aileron driving system, through the linkage design of the aileron and the air brake, the structure is simplified, pneumatic coordination control is achieved, and the control performance at different flight speeds is improved.

The invention relates to an active gravity center adjustment control method for an advanced aircraft variant process, and the method comprises the steps: collecting the variant state information of an aircraft in real time, synchronously obtaining the flight state information, and obtaining an input vector; wherein the variant state information comprises a wing sweep angle, a span-chord ratio and a flap deflection angle, and the flight state information comprises an attack angle, a flight speed, a height, a pitch angle and an acceleration; according to the input vector, an initial pneumatic center position is calculated through a pneumatic center prediction function, and the pneumatic center position is determined according to the pneumatic center position variable quantity; calculating a target gravity center position according to the longitudinal static stability through the average pneumatic chord length of the aircraft and the pneumatic center position, and obtaining a target gravity center position adjustment amount; and adjusting the center of gravity of the aircraft to the target center-of-gravity position through a center-of-gravity adjustment executing mechanism. Active gravity center adjustment can be provided for the advanced aircraft variant process, attitude disturbance is restrained through information collection, pneumatic center calculation and the like, and flight safety is guaranteed.

The invention discloses a flight control method and system for a vertical take-off and landing tilting quad-rotor unmanned aerial vehicle, and aims to solve the technical problem of poor time-varying adaptability of a control system in a nacelle tilting transition stage. According to the method, in a rotor wing flight mode and a nacelle tilting transition mode, attitude angle deviation, deviation change rate and flight speed of the unmanned aerial vehicle are obtained in real time, a fuzzy inference engine is adopted to dynamically output basic control quantity based on a predefined rule base, and a final control instruction is generated in combination with pneumatic feed-forward compensation quantity based on speed to drive a pneumatic control surface. According to the method, fuzzy intelligent decision making and feed-forward dynamic compensation are fused, the limitation that traditional control depends on an accurate model is broken through, the control precision and adaptability of a transition stage are remarkably improved, overshoot is effectively restrained, the adjusting time is shortened, the robustness of a system under model parameter changes and external interference is enhanced, and the system reliability is improved. And a reliable technical guarantee is provided for full-mode flight of the tilt-rotor unmanned aerial vehicle.

The application discloses a kind of support to drag and drop sorting waypoint list management interaction system, belong to ground station waypoint management field.Belong to ground station waypoint management field.It includes waypoint list display module, drag and drop sorting interaction module, waypoint management operation module, associated function adaptation module;Waypoint list display module presents the key parameters such as waypoint height, flight speed with entry form;Drag and drop sorting interaction module supports user to drag and drop waypoint entry adjustment order, real-time feedback state and synchronously flight execution logic;Waypoint management operation module provides waypoint deletion, save, read and upload function;Associated function adaptation module realizes linkage with template editor, relative coordinate editor and flight control module.This system operation is intuitive, data synchronization is efficient, and function integration is strong, effectively solve the problem that traditional waypoint management operation is complicated, data linkage is insufficient, improve the convenience and accuracy of flight mission planning.

The invention discloses an unmanned aerial vehicle defense method, system and device based on electromagnetic interference and physical interception, and relates to the technical field of unmanned aerial vehicle defense. If yes, motion features and appearance features of the unmanned aerial vehicle are obtained, the motion features comprise the flight speed and the linear distance between the unmanned aerial vehicle and the center area of the defense area, and the appearance features comprise the contour and the flight track; determining a current threat level of the unmanned aerial vehicle according to the motion features and the appearance features; according to the current threat level of the unmanned aerial vehicle, the corresponding defense strategy is selected, the accuracy and efficiency of unmanned aerial vehicle defense are improved, the effectiveness and high efficiency requirements of unmanned aerial vehicle defense in a complex scene can be met, and the interception effect is good.

The application discloses a method and system for measuring the flight speed of a UAV in an underground pipeline, comprising: acquiring first video data of an upper pipe wall in a lateral pipeline when the UAV is flying in the lateral pipeline through an upper optical flow sensor; acquiring a first optical flow speed of the UAV according to the first video data; acquiring a first estimated speed of the UAV through a physical model; and fusing the first optical flow speed and the first estimated speed through a Kalman data fusion algorithm to obtain a first estimated speed of the UAV. In the embodiment, the first video data of the upper pipe wall when the UAV is flying in the lateral pipeline is acquired, and a more accurate first optical flow speed can be acquired according to the first video data, thereby solving the problem that when the UAV estimates the flight speed by acquiring image data of a lower pipe wall in an underground pipeline, accumulated water in the underground pipeline affects the optical flow estimation accuracy of the UAV.

The invention discloses a flight plan deployment method, device and equipment, a medium and a program product, and relates to the technical field of flight information, and the technical scheme is characterized in that a flight plan set is received, and a to-be-deployed flight plan is determined from the flight plan set; performing conflict prediction on the to-be-deployed flight plan with the second flight plan and the remaining first flight plans in the flight plan set to generate a conflict prediction result of the to-be-deployed flight plan; adjusting parameters of the flight plans to be deployed according to the conflict prediction result under the performance constraint condition and the airspace risk constraint condition of the aircraft to obtain a plurality of deployed flight plans; the parameters comprise flight time, a flight path and a flight speed; and calculating the cost value of each deployed flight plan according to a preset cost function, selecting the deployed flight plan with the minimum cost value as a deployment result, and issuing the deployment result to the aircraft which does not take off.

The application belongs to the technical field of aircraft simulation, and particularly relates to a flight simulation method and device under gust load. The method comprises the following steps: S1, performing aircraft 1g level flight trimming to determine the aircraft trimming angle of attack and the aircraft trimming elevator deflection; S2, determining the aerodynamic force of the aircraft; S3, performing dynamic response solution on the aerodynamic force of the aircraft to obtain the aerodynamic angle of attack, the flight speed and the pitch angular velocity, and determining the normal overload of the aircraft after superimposing the flight speed on the wind speed of the vertical gust field; S4, obtaining the elevator deflection calculated by the stability augmentation system, superimposing the elevator deflection at a simulation moment to obtain the actual required elevator deflection; and S5, re-determining the aerodynamic force of the aircraft according to the flight speed, the pitch angular velocity, the aerodynamic angle of attack and the actual required elevator deflection until the simulation is completed. The application considers the role of the stability augmentation system in the determination of the aircraft gust load, and the simulation process is simple and has high precision.

The application belongs to the technical field of high Mach number aircraft power system design, and particularly relates to a high Mach number aircraft power system, on the basis of a Mach number two aircraft power system, an APU+EPU scheme is adopted for the auxiliary power system, and a ram turbine is additionally arranged, the auxiliary power system can be used as an emergency energy source when the flight speed exceeds 2 Mach, main engine in-flight accidental shutdown and other abnormal conditions occur, so as to ensure the flight safety of the aircraft, in addition, the auxiliary power system can provide reliable auxiliary energy for the aircraft during aircraft mode conversion and in-flight unpowered taxiing, so as to keep the energy supply stable and ensure the continuous and reliable operation of the aircraft.

This invention discloses an AI-based automatic positioning and navigation system for unmanned aerial vehicles (UAVs), comprising: a UAV body that obtains the starting coordinates through a navigation module by manually inputting the destination coordinates and the planned flight duration t, and then flies from the starting coordinates to the destination coordinates at a flight speed V1 and a flight altitude H1, automatically completing the flight mission and automatically determining the return-to-home status; and a monitoring unit that acquires the initial battery level Q of the UAV body, transmits the initial battery level to the coordinate unit, and monitors the output power of the battery in real time during flight. When abnormal output power occurs during flight, a feedback signal is generated and transmitted to the correction unit. This invention relates to the field of UAV technology and solves the problem that existing UAV automatic positioning and navigation systems do not consider the UAV's battery life, which can easily lead to the UAV being unable to return to home or even crashing during flight.

The invention discloses an air traffic stability control method, which belongs to the technical field of intelligent management of air traffic management, and comprises the following steps: constructing an aircraft flight space-time sequence based on high-altitude cruise aircraft information; calculating a relative flight position interval and a relative flight speed between the aircrafts based on the flight space-time sequence of the aircrafts; based on the relative flight position interval and the relative flight speed between the aircrafts, constructing an aircraft car-following model containing time-delay band bidirectional attention; based on the aircraft car-following model, constructing air traffic stability judgment conditions; and based on the air traffic stability judgment condition, the aircrafts in the unstable state are screened for feedback control, and the air traffic stability is guaranteed. According to the method, the air traffic stability condition can be judged, feedback control is performed by screening aircrafts in an unstable state, and the air traffic stability is guaranteed.

The invention discloses a multi-rotor aircraft with lift-enhanced wings, which comprises rotor power devices, a fuselage for mounting airborne equipment and connecting the rotor power devices, and a pair of independent and controllable lift-enhanced wings, the high-lift wings are mounted on the fuselage; when the multi-rotor aircraft with the high-lift wings flies, a flight control algorithm controls the tilting angles of the high-lift wings according to the attitude and the flight speed of the aircraft, so that the high-lift wings are in a proper attack angle state, and proper lift force can be provided for the aircraft. The flying speed can be increased, the cruising ability is improved, and transportation and storage are convenient.

The invention belongs to the technical field of unmanned aerial vehicle control, and provides an unmanned aerial vehicle linear flight control method and device. The method comprises the following steps: collecting flight state parameters and environment interference parameters of an unmanned aerial vehicle, wherein the flight state parameters comprise at least one of a three-dimensional position, a flight speed, a course angle, a lateral offset distance and an attitude angle; according to the flight state parameters and the environment interference parameters, constructing an unmanned aerial vehicle linear flight dynamics model, linear flight constraint conditions and a fusion reward function; a linear flight simulation environment constructed based on the unmanned aerial vehicle linear flight dynamics model; based on a reinforcement learning algorithm, the linear flight constraint condition and the fusion reward function, performing interactive training on the unmanned aerial vehicle and the linear flight simulation environment to determine a linear flight control strategy; and controlling the unmanned aerial vehicle to fly linearly according to the linear flight control strategy. According to the invention, high-precision, high-stability and energy-saving autonomous linear flight control can be realized, dependence on an accurate model is significantly reduced, and complex environmental interference is effectively coped with.

The invention provides a low-altitude airspace planning and management system and method, relates to the technical field of low-altitude planning, and can solve the technical problem that in the related technology, when an airspace planning and management system carries out route planning, an unmanned aerial vehicle is likely to operate intensively. The low-altitude airspace planning and management system comprises an information acquisition module which is used for acquiring flight information of an unmanned aerial vehicle in an airspace, and the flight information comprises the volume, the flight speed and the initial flight route of the unmanned aerial vehicle; the airspace management module is coupled with the information acquisition module and is used for obtaining the congestion degree information of the airspace according to the flight information; and the flight path planning module is coupled with the information acquisition module and the airspace management module and is used for generating a recommended flight path of the unmanned aerial vehicle according to the flight information and the congestion degree of the airspace.

The application discloses a target direction tracking filtering method based on multi-dimensional motion characteristics. The application combines the actual measurement angle of the target by the directional antenna, the alpha-beta filtering smooth angle, the flight speed of the target, the relative distance of the target, the relative speed vector, the native maneuvering condition and other parameters, and finally comprehensively evaluates the stable target direction. The application is a target direction tracking filtering method based on multi-dimensional motion characteristics. The method can effectively reduce the influence of the interference signal on the target measurement angle without changing the existing TCAS equipment hardware, ensures the accuracy and stability of the target tracking, improves the use satisfaction of the user, and reduces the post-maintenance cost of the equipment after installation.