Beihang University

Beijing Institute of Technology

Xianbin Cao

Wenbo Du

Yumeng Li

Xing Xi

Peng Yang

Xi Bo Zhu

Zhenyu Xiao

Dapeng Wu

Yan Li

Lei Zheng

Liang Xu

Xiangrong Wang

Biyue Li

Xiantong Zhen

Yutao Hu

Jun Zhang

Shuai Wang

Dapeng Li

Chao Yin

Pengcheng Wang

Huayi Wang

Dezhi Zheng

Huaiyuan Liang

Lei Yue

Zehao Xiao

Xiaolong Jiang

Yakun Zhao

Haichao An

Jintong Zhang

Liheng Bian

An atmospheric parameter measuring device includes a cone top, a balance rod an upper propeller, a lower propeller, a transmission module, an accelerating module, a DC generator, an object-carrying shell, an inertial generation module, a wireless energy-carrying communication module, a sensor carrier, a base, an airbag and a low-power-consumption sensor. Electric energy can generate by using four methods of wind energy, microwave, inertia and solar energy, so the energy supply for the carried low-power-consumption sensor can be ensured and the measuring device can generate electric energy at day and night to ensure the working state of the carried low-power-consumption sensor and measure the atmospheric temperature, humidity, wind speed, intensity of pressure and cloud and water grain shape parameters at the current position conveniently; the measuring device can float in the air by the lift force provided by the rotation of the two propellers.

Atmospheric parameter measuring device for air sounding

This application provides an airspace complexity evaluation method based on deep unsupervised learning for air traffic management, which includes the following parts. A stacked autoencoder is used to establish an airspace complexity evaluation model. Input the airspace complexity factors into the stacked autoencoder to obtain the low-dimensional embedded representations of the airspace complexity factors. Cluster the low-dimensional embedded points to capture the centroids of the airspace complexity data. The application utilizes the soft assignment distribution and real assignment distribution of the embedded representations to construct a training loss function which optimizes the airspace complexity evaluation model by gradient descent algorithm. The trained airspace complexity evaluation model and the three obtained cluster centroids describing the airspace complexity level are used to evaluate the current airspace complexity.

Deep unsupervised learning approach, device and storage medium for airspace complexity evaluation

A computer-based airplane flight path planning method based on the pigeon-inspired optimization (PIO) algorithm includes steps of establishing an uncertainty prediction model, determining the path to be optimized, and obtaining an optimal path using the PIO algorithm for a flight controller onboard to execute. The PIO algorithm treats a pigeon flock as a scale-free network, applies map and compass operators to the scale-free network, and performs landmark operations to obtain the optimal path. The device that performs the path planning includes an access module for obtaining the regional environment information and a flight controller onboard the airplane. The flight controller includes a building module for setting up the trajectory prediction model including uncertainties; a determining module to determine the trajectories which need optimization; an optimization module, which uses the PIO algorithm to optimize the flight path; and a computer memory module.

Airplane flight path planning method and device based on the pigeon-inspired optimization

This disclosure provides a cooperative path planning method and device for automatic control aerocraft and aerocraft system. The method includes: determining the current evaluation index of each selected point according to its current position, the target position of the aerocraft and the position of obstacles in the selected area; updating the adjustment displacement of each selected point according to its adjustment displacement, current position and historical optimal position, as well as the global optimal position; updating the current position of each selected point according to its current position and the updated adjustment displacement; after the current position is updated for a set number of times, selecting the position corresponding to the optimal evaluation index from the current positions and global optimal position of all selected points as the next waypoint of the aerocraft.

Cooperative path planning method and device for automatic control aerocraft and aerocraft system

The disclosure provides a dynamic recovery method and system for UAVs and storage medium. On the basis of obtaining the environmental information of UAVs, combined with different factors such as the following task quantity of each UAV, the distance from the current position of each UAV to the recovery point, current performance of each UAV, total cost function is constructed, and optimization is carried out through improved particle swarm optimization algorithm. Then on the premise of the lowest recovery cost, the priority of each UAV is obtained. For the ordered UAV, the route to the recovery point is determined. At the same time, it is necessary to consider conflict resolution of UAVs when encountering obstacles in the flight process, as well as the possible failure in line recovery. At this time, it is necessary to reorder some hovering UAVs to ensure flight safety of UAVs and robustness of the algorithm.

Dynamic recovery method and system for UAVs and storage medium

A sector situation (SS) evaluation framework is based on knowledge transfer and is specifically applicable for small-training-sample environment. The SS evaluation framework is able to effectively mine knowledge hidden within the samples of both target and non-target sectors, and properly handle the integration between the knowledge derived from different sectors. This framework includes three main steps: (1) sufficiently mine the knowledge within the samples of the target sector using the strategies of multi-factor subset generation and multi-base evaluator construction, and build target base evaluators; (2) precisely learn the knowledge in the samples of the non-target sectors using similar strategies for the target sector, together with a sample transformation, and build non-target base evaluators; and (3) efficiently integrate the target and non-target base evaluators based on evaluation confidence analysis of those base evaluators.

Knowledge-transfer-based learning framework for airspace situation evaluation

Provided are a method and device for determining a motion trajectory of a target. The method includes: acquiring a transmitted signal, a first echo signal, and a second echo signal of a frequency modulated continuous wave radar; performing a coherent operation on the transmitted signal and the first echo signal, to determine a beat signal therebetween; performing a two-dimensional fast Fourier transform on the beat signal to determine an initial position of the target; performing a short-time Fourier transform on the beat signal to determine a radial velocity of the target; calculating a difference frequency signal between the first and second echo signals; determining a tangential velocity of the target according to the difference frequency signal; inputting the initial position, the radial velocity, and the tangential velocity to a Kalman filter, and receiving a motion trajectory of the target output by the Kalman filter.

Method and device for determining motion trajectory of target

Provided is a multi-UAV continuous movement control method for energy efficient communication coverage. The method includes: determining observation information at a current moment, the observation information including one or more of the following: energy consumption information of a UAV, coverage indication information of users covered by a UAV network facilitated by the UAV or coverage fairness information of the UAV network; determining control information corresponding to the observation information using a DDPG model according to the observation information, wherein the DDPG model is generated from a DDPG algorithm with sample information as an input, the sample information indicating a mapping relationship between sampled observation information and sampled control information; and controlling the UAV to move according to the control information. The value of the mobile policy from the DDPG model is continuous, thus mitigating the imprecision in existing movement control due to the discreteness in evaluating the mobile policy.

Multi-UAV continuous movement control method, apparatus, device, and storage medium for energy efficient communication coverage

The present disclosure proposes a three-dimensional computational imaging method and apparatus based on a single-pixel sensor, and a storage medium. The method includes the following. A stripe coding is combined with a two-dimensional imaging coding through a preset optical coding to generate a new optical coding, and the new optical coding is loaded into an spatial light modulator (SLM); a two-dimensional spatial information and depth information of a scene are coupled into a one-dimensional measurement value by using a single-pixel detector and the SLM loaded with the new optical coding; and the two-dimensional spatial information and the depth information of the scene are reconstructed, from the one-dimensional measurement value through a decoupling algorithm, for three-dimensional imaging.

Three-dimensional computational imaging method and apparatus based on single-pixel sensor, and non-transitory computer-readable storage medium

Provided are flight conflict resolution method and apparatus based on ultimatum game theory. The method includes: obtaining a first priority of a first aircraft and a second priority of a second aircraft when it is determined that a minimum distance between the first aircraft and the second aircraft within a preset time period is less than a preset distance; determining a first angle and a second angle of the first aircraft and a fourth angle and a fifth angle of the second aircraft according to the first priority, the second priority, and a preset limiting deflection angle; determining a third angle of the first aircraft and a sixth angle of the second aircraft; determining a first deflection angle of the first aircraft and a second deflection angle of the second aircraft according to the first and second priorities, the first, second, third, fourth, fifth and sixth angles.

Flight conflict resolution method and apparatus based on ultimatum game theory

The present disclosure provides a dense crowd counting method and an apparatus, including: acquiring an image to be detected, where the image to be detected includes images of people; feeding the image to be detected into a convolutional neural network model to obtain a crowd density map of the image to be detected; and determining the number of the images of people in the image to be detected according to the crowd density map. Feature information of an image to be detected may be fully extracted through the above mentioned process, thereby realizing a better performance of crowd counting and density estimation, providing great convenience for subsequent security monitoring, crowd control and other applications.

Dense crowd counting method and apparatus

Provided are a network selection method and an apparatus for integrated cellular and drone-cell networks, where the method includes: acquiring a dynamic network model and a dynamic user model, generating a random event vector according to the dynamic network model and the dynamic user model; generating an action vector according to the random event vector; obtaining an individual utility of each user according to the action vector and the random event vector; constructing a first selection model; obtaining the value of the action probability according the first selection model to determine networks that the users choose to access according to the value of the action probability.

Network selection method and apparatus for integrated cellular and drone-cell networks

The present disclosure provides a target tracking method and device oriented to airborne-based monitoring scenarios. The method includes: obtaining a video to-be-tracked of the target object in real time; extracting a first frame and a second frame; trimming and capturing the first frame to derive an image for first interest region; trimming and capturing the second frame to derive an image for target template and an image for second interest region; inputting the image for target template and the image for first interest region into an appearance tracker network to derive an appearance tracking position; inputting the image for first interest region and the image for second interest region into a motion tracker network to derive a motion tracking position; and finally inputting the appearance tracking position and the motion tracking position into a deep integration network to derive a final tracking position.

Target tracking method and device oriented to airborne-based monitoring scenarios

A joint search method for UAV multiobjective path planning in an urban low altitude environment first constructs a static safety index map based on static known obstacles. Meanwhile, based on proactively detected obstacles that are not marked on a geographic map by a UAV, the method constructs online a dynamic safety index map. Second, a multiobjective path planning problem is solved using a joint offline and online search method. Moreover, this method first plans offline the least cost path from a starting point to an end point and then invokes the online search scheme to replan online a changed path when the UAV detects unknown obstacles. Thus, the UAV can avoid dynamic obstacles effectively. The online search scheme has a small search space and can quickly replan a safe path for the UAV, thus satisfying the requirement of UAV on the real-time path planning.

Joint search method for UAV multiobjective path planning in urban low altitude environment

The channel estimation method for millimeter-wave communication includes virtual multipath acquisition and sparse reconstruction. In the virtual multipath acquisition stage, instead of searching the real multipath components (MPCs), the virtual representation of the real MPCs is derived, and the hierarchical search is utilized to acquire the virtual MPCs based on a normal limited-resolution codebook. In the sparse reconstruction stage, the real MPCs are reconstructed from the virtual MPCs acquired in the virtual multipath acquisition stage. An enhanced sub-array scheme is used to design a hierarchical codebook under a strict constant-modulus constraint. The designed codebook can be used for both analog and hybrid beamforming/combining devices. The training overhead of channel estimation and the complexity of the search are significantly reduced, and the size of the dictionary matrix is greatly reduced by exploiting the results of the virtual multipath acquisition.

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Beijing, China

Xianbin Cao