In recent years, UAV networking technology has become a hot topic, and it has been used in industry, agriculture, emergency, fire protection, communications and other fields. Communication technology is the key technology of UAVs, which has a decisive impact on the development of UAVs. The 5G cellular network also brings more possibilities to UAVs. With the rapid development of 5G cellular-connected drones, the security issues of their sensor networks have also attracted widespread attention. This paper studies the key management technology in the 5G network-connected UAV sensor network. Although public key encryption schemes such as RSA and elliptic curves can provide sufficient security, their applications are limited due to their extremely high demands on computing power, which conflict with the resource constraints of sensor nodes. For the security management of cluster keys, this paper introduces the concept of bivariate polynomials into key management. This not only ensures pairwise keys between any two nodes, but also performs intercluster key distribution on the generated bivariate polynomials, tests various characteristics of the generated bivariate polynomials in key management, and Simulation experiments are carried out under different orders of polynomials.
Light and small UAVs have attracted widespread attention due to their good adaptability, low cost, and high temporal resolution. With the continuous development of technology, multi-UAV cooperation has become a research hot spot. The route planning problem of multi-UAV cooperation can be decomposed into two sub-problems: task allocation and route planning. In this paper, a task allocation method based on reinforcement learning is proposed for multi-UAV cooperation. Considering the task requirements, the capabilities of the UAV, the influence of the environment and the conflict of the task, we construct a MDP process include the state space, action space, reward function and discount factor with the constraints and optimization functions. In this paper, the task allocation process is combined with the trajectory planning based on maximizing information throughput, and a large number of simulation tests are carried out to verify the stability of the method.
Hyperspectral images contain large amounts of information and have high spectral resolution. The performance of hyperspectral images in describing and distinguishing target categories has been greatly improved. With the development of unmanned aerial vehicles (UAVs), a lightweight, adaptable and low-cost way has greatly expanded the application field of hyperspectral images. This paper proposes a spatial spectrum attention mechanism based on Deep Deterministic Policy Gradient (DDPG) for hyperspectral classification. This attention mechanism is combined with 3DCNN to assign different weights to different channels in the classification process. The classification accuracy is improved by activating the useful spatial spectrum information and suppressing the useless spatial spectrum information in the hyperspectral image. A large number of experiments have been carried out to prove the effectiveness of the structure.
With the rapid development of drones, unmanned vehicles and robotics industries, VLAM has become a hot technology. In particular, the birth of 5G-powered UAV has promoted the emergence of more industrial applications, making it the most core and indispensable role in many scenarios. The loop closure detection can decrease the accumulative total of error during the process of VSLAM. Former loop closure detection methods always rely on artificially features, which are not robust, making it hard to deal with changing complex scenarios. The later deep learning-based methods are considered to be better solutions for loop closure detection. However, due to the simple network structure, there is still a lot of room for improvement. This paper proposes a more complex neural network to achieve loop closure detection. This approach adopts a fish-shaped deep neural network backbone, which can extract and fuse data features at different levels. Experiments demonstrate the feasibility and effectiveness of this backbone in loop closure detection problems.
With the increase of carrier frequency, bandwidth and antenna size of communication system, the communication system and radar system are gradually approaching. Integration of communication and sensing technology has become a research hotspot in recent years, and can be applied to many fields such as UAV low altitude airspace monitoring and traffic management. This paper provides a comprehensive overview of the latest technologies in the integration of sensing and communication. For different types of integration schemes, the classification and improvement details are given. This paper refines the development and application of communication and sensing in UAV, and provides abundant resources on methods, including the overview of advanced algorithms, systems and applications. This paper can be used as a hands-on guide for understanding, using, and developing methods. This paper comprehensively considers the difficulties in the field of unmanned aerial vehicles, summarizes the challenges faced by the integration of sensing and communication technology, and points out the development direction of this rapid development field.
In the actual operation scenario, we usually want to measure the trajectory of the UAV performing the task. This can be achieved by many different means. For example, we can obtain the motion state estimation of UAV by obtaining sensor information such as GPS, RTK and IMU. Alternatively, the velocity and acceleration of the UAV can be measured and its displacement can be calculated by integration. The device (including hardware and algorithm) to complete this motion estimation is called odometry. Considering the efficiency and cost, we hope to complete the devise of odometry visually. The target of visual odometry (VO) is to estimate the motion of the camera according to the captured image. This paper presents a method based on feature extraction and incremental pose estimation, which can accurately calculate motion state of UAVs through aerial images taken by its own.
UAV has become a promising development direction in 5G era because of its flexible deployment and economic efficiency. UAV with communication function can serve many scenes, such as traffic congestion, limited base station conditions, emergency rescue and so on. However, UAV has limited airborne energy, throughput and energy efficiency are the main bottlenecks of UAV as an air base station. Based on the consideration of various factors such as channel, user, UAV speed and transmission power, this paper constructs a reinforcement learning model for UAV energy efficiency, and puts forward the description of environment matrix to quantify the environmental parameters and participate in the action value evaluation. Firstly, based on the existing conditions, a constrained model is established to maximize the information throughput per unit energy consumption by combining historical empirical data with the exploration of a certain degree of freedom. In addition, we establish strong constraints on UAV energy to avoid unnecessary consumption as much as possible. The experimental results show that the algorithm proposed in this paper shows good performance in the simulation stage and excellent stability in the open environment.
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