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《中国航空学报》2022,35(9):333-341
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. 相似文献
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《中国航空学报》2021,34(2):466-478
With the development of Unmanned Aerial Vehicle (UAV) system autonomy, network communication technology and group intelligence theory, mission execution in the form of a UAV swarm will be an important realization of future applications. Traditional single-UAV mission reliability modeling methods have been unable to meet the requirements of UAV swarm mission reliability modeling. Therefore, the UAV swarm mission reliability modeling and evaluation method is proposed. First, aimed at the interdependence among the multiple layers, a multi-layer network model of a UAV swarm is established. At the same time, based on the system having the following characteristics—using a mission chain to complete the mission and applying the connectivity of the mission network—the mission network model of a UAV swarm is established. Second, vulnerability and connectivity are selected as two indicators to reflect the reliability of the mission, and aimed at random attack and deliberate attack, vulnerability and connectivity evaluation methods are proposed. Finally, the validity and accuracy of the constructed model are verified through simulations, and the model and selected indicators can meet the reliability requirements of the UAV swarm mission. In this way, this study provides quantitative reference for UAV-swarm-related decision-making work and supports the development of UAV-swarm-related work. 相似文献
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一种基于多无人机的中继节点布置问题建模与优化方法 总被引:2,自引:1,他引:1
针对战场环境下急需在无法通信的节点间构建有效通信链路的情形,使用多无人机作为中继节点,建立了中继节点布置(RNP)问题模型。模型以中继链路有效和无人机安全为约束,以中继布置点位置及相应的无人机为输出,不但考虑了使用的中继无人机数量,还考虑了构建中继链路花费的时间。考虑到该问题是难以求解的混合整数多目标优化问题,同时在紧急应用情形下,要求求解算法快速有效,建立了一种多项式时间中继节点布置算法(PTRPA)。仿真实验验证了所提模型确实能够在更短的时间内完成有效中继链路构建;通过Monte-Carlo方法对比和分析不同因素对PTRPA算法、随机抽样算法、遗传算法求解该问题的结果性能和时间性能的影响,验证了PTRPA算法不但能够给出接近最优的解,且快速有效,满足战场决策需求。 相似文献
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基于GA-OCPA学习系统的无人机路径规划方法 总被引:3,自引:2,他引:1
为解决未知空域中无人机路径规划方法实时性和适用性不足的问题,以生物应激条件反射理论为基础,将无人机实时路径规划类比为在外界条件刺激下的一种自学习行为。首先,将概率自动机与遗传算法相结合,设计了基于Skinner操作条件反射理论框架(GA-OCPA)的学习系统;然后,将无人机规避机动的飞行速度、滚转加速度和拉升加速度作为系统学习的行为,并计算每次学习尝试之后的选择概率和个体适应度,通过遗传算法搜索最优行为进而得到最优路径;最后,运用增量多层判别回归树(IHDR)对学习得到的最优行为建立知识库,形成威胁状态与路径规划的匹配映射。实验结果表明GA-OCPA学习系统对于无人机路径规划具备有效性和适用性。 相似文献
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Drogue detection for autonomous aerial refueling based on convolutional neural networks 总被引:2,自引:0,他引:2
Drogue detection is a fundamental issue during the close docking phase of autonomous aerial refueling(AAR). To cope with this issue, a novel and effective method based on deep learning with convolutional neural networks(CNNs) is proposed. In order to ensure its robustness and wide application, a deep learning dataset of images was prepared by utilizing real data of ‘‘Probe and Drogue" aerial refueling, which contains diverse drogues in various environmental conditions without artificial features placed on the drogues. By employing deep learning ideas and graphics processing units(GPUs), a model for drogue detection using a Caffe deep learning framework with CNNs was designed to ensure the method's accuracy and real-time performance. Experiments were conducted to demonstrate the effectiveness of the proposed method, and results based on real AAR data compare its performance to other methods, validating the accuracy, speed, and robustness of its drogue detection ability. 相似文献
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多植保无人机协同路径规划 总被引:2,自引:0,他引:2
为实现多植保无人机(UAVs)协同作业,并提高作业效率,提出了一种基于改进粒子群优化(PSO)的多植保无人机协同路径规划算法。根据作业区域的形状面积和植保UAV的作业参数划分各架UAV作业区域,采用栅格法生成各区域全覆盖作业航线。以各架植保UAV各架次植保作业距离为算法寻优变量,在确保各架UAV补给时间满足间隔分布约束条件下,综合考虑补给总次数、返航补给总时间、总耗时和最小补给时间间隔4项因素,并构成目标函数,通过采用改进PSO算法,实现了对各UAV返航顺序和返航点位置的寻优。仿真分析结果表明,相较于最大作业距离规划和最小返航距离规划,本文提出的规划算法表现出了较优的性能和较好的作业区域适应性,证实了其有效性和实用性。 相似文献