无人集群试验评估现状及技术方法综述

doi:10.12305/j.issn.1001-506X.2024.02.21

摘要/Abstract

摘要：

试验评估是促进装备系统作战能力生成和实战化应用的重要手段。无人集群依靠自组网实现复杂交互, 具备典型的智能性和涌现性, 开展无人集群试验评估研究面临着指标不清、标准模糊、技术方法落后等难题。为了进一步推动无人集群试验评估理论研究, 对国内外已开展的无人集群试验评估相关规划和项目实践现状进行了概述。面向评估指标设计和评估方法研究两个试验评估关键环节, 首先对已有无人集群评估文献中使用的指标进行了分类梳理, 并分析了现有研究在指标选取和构建方面的特点与不足; 然后, 结合不同无人集群关键技术研究中涉及的评价指标, 提出了面向无人集群关键技术能力的评估指标设计思路。在此基础上, 根据具体含义及计算方式, 将已有指标划分为基础指标和综合指标2类, 分类介绍了可用的评估方法, 期望为后续无人集群试验评估的指标构建和评估方法选取工作提供一定借鉴。

关键词: 无人集群, 试验评估, 评估指标, 评估方法

Abstract:

Test evaluation is an important means to promote the generation of combat capability and practical application of equipment system. Unmanned swarms rely on self-organized network to realize complex interaction, which have the typical characteristics of intelligence and emergence. The research on unmanned swarm test evaluation is faced with problems such as unclear indicators, fuzzy standards, backward technology methods and so on. In order to promote further theoretical researches, this study summarizes the current status of relevant planning and project practice at home and abroad. The two key links of test evaluation, evaluation index design and evaluation method research are elaborated. Firstly, the indexes used in existing literature on unmanned swarm evaluation are sorted out, and the characteristics and shortcomings of the index selection and construction in current researches are analyzed. Then, combined with the evaluation indexes involved in the researches of different key technologies, the design idea of evaluation indexes for key technology capabilities of unmanned swarm is proposed. On this basis, according to the specific meaning and calculation method, the existing indexes are divided into basic indexes and comprehensive indexes, and the available evaluation methods are introduced. This study is expected to provide some reference for the construction of indexes and the selection of evaluation methods for the subsequent unmanned swarm test evaluation.

Key words: unmanned swarm, test evaluation, evaluation index, evaluation method

中图分类号:

V217

赵蕊蕊, 于海跃, 游雅倩, 张涛, 陶敏, 姜江. 无人集群试验评估现状及技术方法综述[J]. 系统工程与电子技术, 2024, 46(2): 570-585.

Ruirui ZHAO, Haiyue YU, Yaqian YOU, Tao ZHANG, Min TAO, Jiang JIANG. Review on current status and technology method of unmanned swarm test evaluation[J]. Systems Engineering and Electronics, 2024, 46(2): 570-585.

图/表 13

表1

国外无人集群试验评估项目实践汇总"

国家	项目名称	时间/年份	主要研究内容梳理
美国	灰山鹑项目^[28]	2011	超微型、协同自主的无人集群相关技术; 监视侦察、赛博攻击、电子干扰等任务
美国	体系综合技术和试验^[29]	2014	无人装备及无人集群新技术的系统集成; 多平台整体作战效能的提升
美国	拒止环境中协同作战项目^[30]	2014~2020	集侦察、监视与打击于一体的无人机集群; 复杂或高强度干扰环境; 飞行和大规模自主试验
美国	班组X试验^[31]	2014至今	无人机、无人车、先进传感器和机器学习等新技术
美国	快速轻型自主性项目^[32]	2015~2018	自主算法; 室内、地下或人为干扰等无法使用全球定位系统的环境
美国	小精灵项目^[33]	2015~2018	分布式空战、可回收等集群技术; 情报侦察、电子攻击或空间定位任务
美国	低成本无人机集群技术^[34]	2016	快速释放大量小型无人机; 自适应组网和自主协同; 空中监视、护航、饱和攻击等任务
美国	忠实僚机项目^[25]	2016	改装F-16战机为具有一定自主能力的无人驾驶飞机, 搭配F-35有人机形成长机-多僚机编队
美国	无人集群试验^[5]	2016	4艇协同对海目标自主察打跟踪试验; 美国成为首个实现水面无人艇集群自主作战的国家
美国	微小水下探索者集群^[35]	2017	模仿海洋浮游生物行为; 海底三维空间; 采集温度信息
美国	近战隐蔽自主一次性无人机项目^[36]	2017	低成本一次性微型无人机; 区域内气象资料收集、核生化区域检查、侦察、情报搜集等任务
美国	进攻性蜂群使能战术^[34]	2016~2020	小型无人机集群与地面机器人和地面部队的配合; 复杂城市环境; 防御、火力、精确打击效果及情报侦察能力
美国	水下微小无人机集群^[37]	2018	海洋环境监控、羽流跟踪、三维数据网络收集、自主声探等; 已成功应用于协助海军搜寻
欧洲	多异构无人机实时协同和控制项目^[38]	—	多异构无人机组成的协同探测和监视系统; 分布式控制结构; 分布式信息感知和实时图像处理技术集成
欧洲	面向安全无线的高移动性协同工业系统的估计与控制项目^[39]	2011	高动态固定翼和旋翼无人机; 预测和协同控制技术; 多机自动感知、规避与精确着陆问题
欧洲	集体认知机器人^[40]	2015	41个水下机器人组成的集群; 当时全球数量最多
英国	无人集群试验^[41]	2020	即插即用开放式架构和智能互联技术; 20架固定翼无人机、异构无人机蜂群; 超视距飞行等技术验证
印度	无人集群概念项目^[42]	2019	战斗机发射大量察打一体无人机; 对地防空打击任务

表1

表2

图1

表3

无人集群综合评估指标体系梳理"

性能	评估内容	评估指标
感知	侦察能力	侦察范围/侦察覆盖率/集群部署熵^[55]、侦察高度、总航程、总航时^[52]
	侦察能力	目标发现能力、目标识别能力、目标定位能力、目标跟踪能力^[67]
	态势感知能力	雷达探测能力、光电探测能力、电子侦察能力、信息融合能力^[64-65]
	态势感知能力	完备性-区域覆盖率/目标数量完备性; 准确性-航迹真伪性(虚假航迹/冗余航迹)/目标离散属性(目标类型/种类/敌我属性)/目标连续属性(目标位置/速度/航向角), 连续性-航迹批号改变率/最长航迹段比例, 时效性-信息获取延时, 相关性, 共享度^[56-58]
分析	目标检测算法	AUC^*、计算时间、环境背景、目标大小、摄像视角、光照及遮挡条件^[49]
决策	指挥控制能力	指挥控制效率/系统生存能力^[61]、航迹规划能力/任务分配能力^{[8, 51]}
	判断决策能力	目标识别能力、目标分配能力、航路规划能力^[67]
	系统编配方式	参战人员编制, 任务机/中继机/机载任务设备数量, 指挥控制车/机动测控车/地面数据终端/机载无线电设备数量^[53-54], 维修车/运输车/发射车数量^[52]
行动	协同能力	协同时间、协同信息量^[53-54], 协同可信性、协同可靠性^[61]
	编队集结能力	快速性-最小集结时间/协调一致时间, 准确性-末端位置误差/末端姿态误差/末端速度误差/航迹匹配表, 稳定性-平均故障处理时间/平均无故障时间/可靠性指数, 安全性-探测半径/防撞时间代价/地面撞击安全水平^[46]
	组队能力	飞行性能、战术编队、飞机可用度、生存力^[64-65]
	生存能力	被雷达发现概率, 机动性(最小转弯半径)/隐身性(电子对抗能力), 基本性能(实用升限、航程、巡航速度、载荷能力)^{[8, 46]}
	攻击能力	火力对抗能力、规避机动性、电子对抗能力^[61], 目标探测能力、目标识别能力、目标毁伤能力-各种武器杀伤概率^[61], 抗干扰能力、毁伤能力^[64], 命中精度^[50]
	整体作战能力	作战环数量、信息熵^[59], 火力单元数量、可靠性、生存能力、搜索能力、毁伤能力、抗干扰能力^[60]
	协同作战效能	防御成功率, 侦察/攻击/诱饵无人机存活率、目标摧毁率^[66], 毁伤效果^[62]
	通信能力	数据传输速率、传输延迟时间、误码率、丢包率、延迟违反概率、端与端距离^[46-48], 时效性、有效系统容量、节点连通性^[64]

表3

图2

表4

图3

表5

表6

图4

图5

图6

表7

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项目	时间	主要研究内容梳理
集群式箱式发射折叠翼无人机系统^[23]	2016	先进的小型无人机集群作战系统
集群飞行试验^[21]	2016~2017	67架、119架、200架固定翼无人机集群飞行; 编队弹射起飞、空中集结多目标分组、编队合围等动作
集群技术研究及试验^[43]	2018	20架无人机组成的无人机集群自主协同飞行试验; 自适应分布体系架构、任务规划、并行感知等关键技术
集群技术研究及试验^[44]	2018	基于狼群行为机制的无人机协同任务分配飞行试验; 目标分配、目标跟踪、集群围捕等技术
集群跨域协同试飞试验^[41]	2020	陆空协同固定翼无人机蜂群系统相关试飞试验; 对地察打、精确打击等任务能力
无人艇协同试验^[5]	2018	4艘水面无人艇组网; 协同勘探、巡逻缉私、污染清理等试验
无人艇协同演练^[5]	2018	世界最大规模(56艘)水面无人艇群协同演练; 快速集结、队形保持、动态任务分配、队形自主变换、协同避障及容错控制等多项测试科目
水下航行器研发^[45]	1992~2019	“探索者”“CR01”“CR02”“潜龙一号”“潜龙二号”一系列航行器
小型自主水下航行器研发^[45]	“十二五”	300 kg级自主水下航行器; 海域110 km的自主航行和自主布防等功能, 潜深可达1 000 m

能力	评估内容	评估指标
任务分配能力	自身性能	作战高度、作战半径、航程、通信距离、侧向加速度、载荷能力
	集群资源	武器资源、油量
	算法性能	计算时间、迭代次数、任务完成时间、任务分配收益/目标价值收益/目标覆盖数量、航程代价

能力	评估内容	评估指标
路径规划能力	集群自身性能	航程、安全距离、飞行速度、飞行高度、转弯角、俯冲角、爬升角
	任务环境	障碍威胁程度、飞行边界、地形边界
	算法性能	计算时间、迭代次数、航行时间、路径长度、任务完成时间、整体工作效益/任务完成数量、路径重合度、结果稳定性

评估方法	方法特点	数据	适用情景	已有应用
AHP	建立层次结构, 定量与定性分析结合	定性定量	确定指标权重; 不同方案的对比评价	作战能力评估^[57]、作战效能评估^[61]
模糊综合评价法	结果清晰, 能较好地解决模糊的、难以量化的问题	定性	多准则评价; 多方案寻优	基于鲁棒性评估的决策^[117]
神经网络	具有自主学习与调整能力, 客观性强	定量	图像特征对比; 数据量大且输入输出已知	作战效能评估^[63]
ADC法	考虑问题全面, 数学模型严谨	定量	评估在规定条件下满足特定任务要求的程度	作战效能评估^[65]
DEA法	客观性强	定量	多输入多输出的评估与排序	系统编配方案评估^[52]
贝叶斯网络	可以处理不确定信息, 推理能力强且速度快, 理论基础扎实	定性定量	分析评估目标的优劣及各因素的影响程度; 动态变化场景	作战效能评估^[8]
证据推理规则	可以融合专家经验, 处理各类不确定信息	定性定量	数据类型多样、存在不确定性; 多源数据或多时间点数据融合	作战能力评估^[58]

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