首页|基于静动态综合估计的无人机蜂群态势感知技术

基于静动态综合估计的无人机蜂群态势感知技术

扫码查看
随着军用无人机技术的快速发展,其作战体系也逐渐从无人机单机作战向蜂群作战转变.为了实现入侵无人机蜂群的反制,亟需探索面向无人机蜂群入侵场景的态势感知技术.目前,无人机蜂群态势感知领域主要在整体概念层面研究态势感知的技术与模型,缺乏具体应用场景下的态势感知技术与仿真平台.本文面向分布式作战战术场景与哈里斯鹰的合围狩猎方式,引入了无人机蜂群的进攻阵型与仿鹰群的无人机蜂群协同攻击模型,使用到达时间差定位算法与联合概率数据关联滤波器获得无人机蜂群轨迹信息,并将无人机蜂群战场态势划分为静态态势与动态态势,分别表示对战场某个时刻态势的理解与战场未来趋势的推断.在此基础上,本文提出了一种基于静动态综合估计的无人机蜂群态势感知技术,涵盖基于模糊规则评价的静态态势感知以及基于蜂群预测推断的动态态势感知.同时,搭建了三维无人机蜂群态势感知仿真平台,实现了入侵无人机蜂群的运动学建模、定位跟踪、战场态势感知以及误差分析等过程,验证了所提态势感知技术的有效性.
UAV Swarm Situational Awareness Technology Based on Static and Dynamic Comprehensive Estimations
With the rapid development of military unmanned aerial vehicle(UAV)technology,its combat system is gradually changing from single to swarm.To counteract the invasion of UAV swarms,it is critical to explore situational awareness technologies tailored for the scenario involving UAV swarm incursions.Currently,UAV swarm situational awareness field primarily focuses on the conceptual study of technologies and models,however,it lacks specific situ-ational awareness technologies and simulation platforms tailored for concrete application scenarios.This paper intro-duces the attack formations of UAV swarm and the cooperative attack model of UAVs based on distributed warfare tacti-cal scenarios and Harris hawk's herding hunting model.The time difference of arrival(TDOA)and the joint probabilis-tic data association filter(JPDAF)are used to obtain the trajectory of the UAV swarm.The battlefield situation for the UAV swarm can be categorized into static and dynamic,representing the understanding of the battlefield situation at a given moment and the inference of future trends on the battlefield,respectively.On this basis,a UAV swarm situational awareness technology based on static and dynamic comprehensive estimations are proposed,including static situational awareness evaluated by fuzzy rules and dynamic situational awareness inferred from swarm predictions.Additionally,a three-dimensional UAV swarm situational awareness simulation platform is developed,where the processes of kinematic modeling,positioning tracking,battlefield situational awareness,and error analysis of invading UAV swarms are real-ized to verify the effectiveness of the proposed situational awareness technology.

UAV swarmstatic and dynamic comprehensive estimationssituational awarenessmulti-target tracking

彭鸿飞、朱鑫潮、周成伟、史治国

展开 >

浙江大学信息与电子工程学院,浙江杭州 310027

浙江省协同感知与自主无人系统重点实验室,浙江杭州 310015

浙江大学金华研究院,浙江金华 321037

无人机蜂群 静动态综合估计 态势感知 多目标跟踪

国家自然科学基金区域创新发展联合基金重点项目浙江大学教育基金会启真人才基金

U21A20456

2024

信号处理
中国电子学会

信号处理

CSTPCD北大核心
影响因子:1.502
ISSN:1003-0530
年,卷(期):2024.40(5)
  • 39