大规模无人机集群通信定位一体化技术  

Integrated Technology of Communication and Positioning in Large-scale UAV Clusters

作　　者：樊邦奎 刘德康[1] 张瑞雨 唐家玮 常添 FAN Bangkui;LIU Dekang;ZHANG Ruiyu;TANG Jiawei;CHANG Tian(Intelligent Collaborative Perception and Analysis Cognitive Laboratory,Beijing 100094,China;Chinese Academy of Engineering,Beijing 100088,China;School of Electronic Information Engineering,Beihang University,Beijing 100083,China;School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]智能协同感知与分析认知实验室,北京100094 [2]中国工程院,北京100088 [3]北京航空航天大学电子信息工程学院,北京100083 [4]北京理工大学信息与电子学院,北京100081

出　　处：《信号处理》2024年第1期7-16,共10页Journal of Signal Processing

基　　金：创新团队自主科研课题。

摘　　要：大规模无人机(Unmanned Aerial Vehicle,UAV)集群通过部署大量低成本无人机,依靠协同感知、信息共享和分工协调完成各类复杂任务,具备高度的智慧性和自主性,已逐渐成为无人机集群技术未来的发展趋势。大规模无人机集群十分依赖高精度定位技术,以维持集群稳定、避免相互碰撞、实现目标指引。然而,在现有的主要导航定位方式中,微机电(Micro-Electro-Mechanical System,MEMS)惯性导航存在严重的累积误差,而复杂电磁环境下卫星导航易受到干扰,均难以实现高精度相对定位和长时间全局定位。针对这一问题,本文依靠通信测距技术,实现无人机集群内部高精度相对测距,修正惯导定位误差,为无人机集群在卫星导航拒止条件下执行任务提供高精度导航定位服务。本文建立了惯性导航定位模型和通信测距信号模型,构造了基于最大后验概率(Maximum A Posteriori,MAP)方法的融合定位问题,推导得到了该定位问题的克拉美-罗下界(Cramér-Rao Lower Bound,CRLB),并提出了一种基于高斯牛顿优化的全信息融合定位方法。针对长距离飞行中,测距精度远高于惯导定位精度的场景,本文提出了一种基于优超函数算法(Scaling-by-Majorizing-A-Complicated-Function,SMACOF)的高精度测距辅助定位方法,通过建立高精度相对坐标和最优变换参数估计,实现最优位置估计,并分析了该方法的性能改进程度。最后,通过数值仿真验证本文所提出的方法能够将融合后的定位标准差降低至惯导定位标准差的√2/N,能够渐进达到CRLB性能。Large-scale unmanned Aerial Vehicle(UAV)swarms rely on collaborative awareness,information exchanging,and collaboration to complete complex tasks.With high intelligence and autonomy,UAV swarm has gradually become the future development trend of UAV technology.At present,large scale UAV swarm is closely combined with high-precision positioning technology.UAVs rely on high-precision relative positioning to maintain cluster stability and avoid collisions,and the entire UAV swarm relies on global navigation and positioning to achieve target guidance.In order to reduce costs,large scale UAV swarms generally use micro-electro-mechanical system(MEMS)inertial navigation systems and satellite navigation systems.However,in the existing main navigation positioning methods,there are serious cumulative errors in MEMS inertial navigation,and satellite navigation in complex electromagnetic environments is easily disturbed,both of which are difficult to achieve high-precision relative positioning and long-term global positioning.To address this issue,this paper proposes a fusion positioning method that relies on communication ranging technology to correct the error of inertial navigation and achieve high-precision relative distance measurement within UAV swarms,so as to provide high-precision navigation and positioning services for UAV swarms to perform tasks under satellite navigation refusal conditions.It can not only improve the relative positioning accuracy within the UAV cluster,but also improve the global positioning accuracy of the entire UAV cluster.In this paper,an inertial navigation positioning model and a communication ranging signal model are first established respectively,then a fusion localization problem based on the Maximum A Posteriori(MAP)method is constructed,and the optimal estimation problem of fusion positioning is constructed by the fusion of communication signal and inertial guide data.Secondly,based on the independence of inertial navigation and communication ranging,this paper derives the ranging accur

关 键 词：无人机集群 通信定位一体化 全信息融合定位 

分 类 号：TN95[电子电信—信号与信息处理]