基于航空照相枪的数字暗盒设计

航空照相枪是一种安装在歼击机上的小型电影摄影机。传统照相枪主要存在判读环境受限制、判读准确度低、工作可靠性差、效率低等问题。设计了由百万像素CMOS图像传感器、大容量闪存FLASH和CPLD为主要部件组成的数字暗盒。使用基于CPLD状态机设计技术,4片闪存分时存储数据,实现无缓存无压缩实时存储,可以有效地提高判读效率、准确度问题,并使计算机自动判读成为可能。     

Highly reliable relative navigation for multi-UAV formation flight in urban environments

Formation flight of multiple Unmanned Aerial Vehicles (UAVs) is expected to bring significant benefits to a wide range of applications. Accurate and reliable relative position information is a prerequisite to safely maintain a fairly close distance between UAVs and to achieve inner-system collision avoidance. However, Global Navigation Satellite System (GNSS) measurements are vulnerable to erroneous signals in urban canyons, which could potentially lead to catastrophic consequences. Accordingly, on the basis of performing relative positioning with double differenced pseudoranges, this paper develops an integrity monitoring framework to improve navigation integrity (a measure of reliability) in urban environments. On the one hand, this framework includes a fault detection and exclusion scheme to protect against measurement faults. To accommodate urban scenarios, spatial dependence in the faults are taken into consideration by this scheme. On the other hand, relative protection level is rigorously derived to describe the probabilistic error bound of the navigation output. This indicator can be used to evaluate collision risk and to warn collision danger in real time. The proposed algorithms are validated by both simulations and flight experiments. Simulation results quantitatively reveal the sensitivity of navigation performance to receiver configurations and environmental conditions. And experimental results suggest high efficiency and effectiveness of the new integrity monitoring framework.     

Optimal trajectory and downlink power control for multi-type UAV aerial base stations

Unmanned Aerial Vehicles (UAVs) enabled Aerial Base Stations (UABSs) have been studied widely in future communications. However, there are a series of challenges such as interference management, trajectory design and resource allocation in the scenarios of multi-UAV networks. Besides, different performances among UABSs increase complexity and bring many challenges. In this paper, the joint downlink transmission power control and trajectory design problem in multi-type UABSs communication network is investigated. In order to satisfy the signal to interference plus noise power ratio of users, each UABS needs to adjust its position and transmission power. Based on the interactions among multiple communication links, a non-cooperative Mean-Field-Type Game (MFTG) is proposed to model the joint optimization problem. Then, a Nash equilibrium solution is solved by two steps: first, the users in the given area are clustered to get the initial deployment of the UABSs; second, the Mean-Field Q (MFQ)-learning algorithm is proposed to solve the discrete MFTG problem. Finally, the effectiveness of the approach is verified through the simulations, which simplifies the solution process and effectively reduces the energy consumption of each UABS.     

Periodic event-triggered formation control for multi-UAV systems with collision avoidance

In this paper, periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles (UAVs). Firstly, based on the Artificial Potential Field (APF) method, a novel sliding manifold is proposed for controller design, which can solve the problem of collision avoidance. Then, the event-triggered strategy is applied to the distributed formation control of multi-UAV systems, where the evaluation of the event condition is continuous. In addition, the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound. Next, a periodic event-triggered mechanism is developed for formation control based on the continuous event-triggered mechanism. The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors, which can reduce the control cost. The stability of the control system is proved by the Lyapunov function method. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol.     

Target localization based on cross-view matching between UAV and satellite

Matching remote sensing images taken by an unmanned aerial vehicle (UAV) with satellite remote sensing images with geolocation information. Thus, the specific geographic location of the target object captured by the UAV is determined. Its main challenge is the considerable differences in the visual content of remote sensing images acquired by satellites and UAVs, such as dramatic changes in viewpoint, unknown orientations, etc. Much of the previous work has focused on image matching of homologous data. To overcome the difficulties caused by the difference between these two data modes and maintain robustness in visual positioning, a quality-aware template matching method based on scale-adaptive deep convolutional features is proposed by deeply mining their common features. The template size feature map and the reference image feature map are first obtained. The two feature maps obtained are used to measure the similarity. Finally, a heat map representing the probability of matching is generated to determine the best match in the reference image. The method is applied to the latest UAV-based geolocation dataset (University-1652 dataset) and the real-scene campus data we collected with UAVs. The experimental results demonstrate the effectiveness and superiority of the method.     

Green UAV communications for 6G: A survey

Unmanned Aerial Vehicles (UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation (6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly, serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus, UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical challenge. In this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.     

Design,modeling, and control of morphing aircraft:A review

A morphing aircraft can adapt its configuration to suit different types of tasks, which is also an important requirement of Unmanned Aerial Vehicles(UAV). The successful development of an unmanned morphing aircraft involves three steps that determine its ability and intelligent: configuration design, dynamic modeling and flight control. This study conducts a comprehensive survey of morphing aircraft. First, the methods to design the configuration of a morphing aircraft are presented and analyzed...     

Adaptive vector field based accurate homing control of aerial delivery systems

This paper investigates the homing control problem of a flexible aerial delivery system with external wind, at-mospheric turbulence, and aerodynamic uncertainties. An accurate homing control strategy is presented, consisting of a trajectory generation algorithm and a lateral tracking controller. A high-altitude trajectory generation is de-veloped with system characteristics explicitly considered to generate the desired trajectory for the aerial delivery control system design. It significantly compensates for the altitude deviation of the existing multiphase theory based trajectory generation methodologies. A novel adaptive vector field control law is then designed to accom-plish the lateral tracking maneuvers. The key feature of the proposed method is that the complete lateral closed-loop control, including position and heading angle loops, is achieved in the presence of disturbances and dynamic uncertainties. The homing control with high landing accuracy is therefore achieved. Simulation and hardware-in-loop testing results are finally presented to validate the proposed method’s effectiveness compared to a conventional homing control scheme.     

Comparison of residual and dense neural network approaches for building extraction from high-resolution aerial images

Applications including change detection, disaster management, and urban planning require precise building information, and therefore automatic building extraction has become a significant research topic. With the improvements in sensor and satellite technologies, more data has become available, and with the increased computational power, deep learning methods have emerged as successful tools. In this study, U-Net and FPN architectures using four different backbones (ResNet-50, ResNeXt-50, SE-ResNext-50, and DenseNet-121), and an Attention Residual U-Net approach were used for building extraction from high-resolution aerial images. Two publicly available datasets, Inria Aerial Image Labeling Dataset and Massachusetts Buildings Dataset were used to train and test the models. According to the results, Attention Residual U-Net model has the highest F1 score with 0.8154, IoU score with 0.7102, and test accuracy with 94.51% on the Inria dataset. On the Massachusetts dataset, FPN Dense-Net-121 model has the highest F1 score with 0.7565 and IoU score with 0.6188, and Attention Residual U-Net model has the highest test accuracy with 92.43%. It has been observed that, FPN with DenseNet backbone can be a better choice when working with small size datasets. On the other hand, Attention Residual U-Net approach achieved higher success when a sufficiently large dataset is provided.     

Distortion minimization for multimedia transmission in NOMA HAP-UAV integrated aerial access networks

Aerial access networks have been envisioned as a promising 6G solution to enhance the ground communication systems in both coverage and capacity. To better utilize the spectrum and fully explore different channel characteristics, this paper constructs an integrated network comprising the High Altitude Platform (HAP) and Unmanned Air Vehicles (UAVs) with the Non-Orthogonal Multiple Access (NOMA) technology. In order to improve the transmission quality of images and videos, a power management scheme is proposed to minimize the distortion of the transmissions from the HAP and UAVs to the terminals. The power control is formulated as a non-convex problem constrained by the maximal transmit power and the minimal terminal rate requirements. The variable substitution and the first-order Tailor’s expansion is used to transform it into a sequence of convex problems, which are subsequently solved through the gradient projection method. Simulation demonstrates the signal distortion and error rate improvement achieved by the proposed algorithm.