智能机器人、计算机和通信等领域的高速发展，使得无人机(Unmanned aerial vehicle, UAV)在军事和民用领域得到了广泛应用，但单架无人机由于其自身软硬件条件的限制，已很难满足日益复杂的工作环境和任务需求，而无人机集群不仅能高效率的发挥单体无人机配置灵活和低成本的优点，还能减少因单架无人机功能单一造成的不良影响。目前，大部分对无人机集群的研究都集中在优化网络协议和协同定位、编队控制、以及任务分配等算法上，但不可忽略的是，对无人机集群在复杂环境下的运行进行可靠性评估是任务决策的理论支撑。自组网和协同定位作为无人机集群运行的基础保障，其可靠性研究是无人机集群可靠性评估的关键步骤，因此，本文重点研究无人机集群自组网与协同定位的可靠性，主要研究工作包括以下几个方面：

无人机集群自组网可靠性评估方面，考虑无人机集群在执行任务时对信息交互的时效性需求，提出一种以自组网性能满足任务需求能力的可靠性评估方案。首先，综合考虑信道竞争、数据包传输速度和环境等因素对消息传输延迟的影响，并通过排队论建立数据包传输延迟模型。然后，以消息传输需要时间小于可用时间的概率作为评估指标建立消息传输可靠性模型。最后，仿真分析编队队形、无人机数量、编队缩放因子、传输速度、信息交互强度和环境干扰对消息传输可靠性的影响。仿真结果可为无人机集群执行不同任务时选择编队队形以及集群体系配置提供一定的参考依据。

无人机集群协同定位可靠性评估方面，考虑无人机集群在执行任务时对位置服务的需求，对基于测距的协同定位系统提出一种以估计的相对位置满足任务需求能力的可靠性评估方案。首先，基于测距信息质量和数量两个维度建立观测距离信息模型，其次，通过对协同定位的误差抑制能力分析，建立异常数据清除能力的评估模型。然后，以无人机之间的相对位置距离误差小于给定阈值的概率作为评估指标建立协同定位可靠性模型。最后，仿真分析环境因素、集群配置以及异常数据清除能力对协同定位可靠性的影响。仿真结果可为协同定位系统的配置以及可靠性优化提供思路。

无人机集群自组网与协同定位的可靠性优化方面，通过分析自组网与协同定位可靠性的影响因素，提出可靠性优化备选方案的设计思路，并建立有效备选方案模型。然后，通过费用模糊化建立基于费效分析的可靠性优化模型。最后，通过实例表明其可靠性优化模型能在不同情形下为决策者选择符合期望的最优可靠性优化方案。

The rapid development of intelligent robots, computers, and communication fields has led to the widespread application of unmanned aerial vehicle (UAV) in military and civilian fields. However, due to the limitations of their own software and hardware conditions, single UAV is difficult to meet the increasingly complex working environment and task requirements. UAV cluster can not only efficiently utilize the advantages of flexible configuration and low cost of single UAV, it can also reduce the adverse effects, which caused by single UAV functionality. At present, most research on UAV cluster focuses on optimizing network protocols and algorithms such as collaborative positioning, formation control, and task allocation. However, it cannot be ignored that reliability evaluation of UAV cluster operating in complex environments is the theoretical support for task decision-making. As the basic guarantee for the operation of UAV cluster, the research on the reliability of ad hoc network and collaborative positioning is a key step in the reliability evaluation of UAV cluster. Therefore, this paper focuses on the reliability of ad hoc network and collaborative positioning of UAV cluster. The main research work includes the following aspects:

 

In terms of reliability evaluation of ad hoc network for UAV cluster, this paper proposes a reliability evaluation scheme that satisfies the task requirements based on the performance of ad hoc network, taking into account the timeliness requirements of information exchange during task execution. Firstly, a packet transmission delay model was established through queuing theory, taking into account the effects of channel competition, packet transmission speed, and environment on message transmission delay; Then, a reliability model for message transmission was established based on the probability that the required time for message transmission is less than the available time as an evaluation indicator. Finally, the effects of formation, number of UAV, formation scaling factor, transmission speed, information interaction intensity, and environmental interference on the reliability of ad hoc network was simulated and analyzed. The simulation results can provide a certain reference basis for selecting formation formations and cluster system configurations when unmanned aerial vehicle clusters perform different tasks.

 

In terms of reliability evaluation of collaborative positioning for UAV cluster, this paper proposes a reliability evaluation scheme for range-based collaborative positioning systems, taking into account the demand for location service when UAV cluster perform tasks. Firstly, an observation distance information model was established based on the quality and quantity dimensions of ranging information. Secondly, an evaluation model for the system's ability to clear abnormal data was established by analyzing the error suppression capability of collaborative positioning. Then, the probability of the relative position distance error between UAV being less than a given threshold was used as an evaluation indicator to establish a collaborative positioning reliability model. Finally, the impact of environmental factors, UAV cluster configuration, and abnormal data clearing ability on the reliability of collaborative positioning was simulated and analyzed. The simulation results can provide ideas for the configuration and reliability optimization of the UAV cluster collaborative positioning system.

 

In terms of reliability optimization of ad hoc network and collaborative positioning for UAV cluster, this paper analyzes the factors that affect the reliability of ad hoc network and collaborative positioning, proposes design ideas for reliability optimization alternatives, and establishes an effective alternative model. Then, a reliability optimization model based on cost-effectiveness analysis is established through fuzzy costing. Finally, an example shows that its reliability optimization model can select the optimal reliability optimization scheme that meets the expectations for decision-makers in different situations.

[1] MCENROE P, WANG S, LIYANAGE M. A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges[J]. IEEE Internet of Things Journal, 2022, 9(17): 15435-15459.
[2] 谷旭平, 唐大全, 唐管政. 无人机集群关键技术研究综述[J]. 自动化与仪器仪表, 2021, 258(4): 21-26.
[3] 王祥科, 陈浩, 赵述龙. 大规模固定翼无人机集群编队控制方法[J]. 控制与决策, 2021, 36(9): 2063-2073.
[4] 牛轶峰, 肖湘江, 柯冠岩. 无人机集群作战概念及关键技术分析[J]. 国防科技, 2013, 34(5): 37-43.
[5] 韩月明, 方丹, 张红艳. 无人机集群典型作战运用样式及关键技术分析[J]. 飞航导弹, 2020(9): 43-27.
[6] RUFA J R, ATKINS E M. Unmanned aircraft system navigation in the urban environment: A systems analysis[J]. Journal of Aerospace Information Systems, 2016, 13(4): 143-160.
[7] SKOROBOGATOV G, BARRADO C, SALAMÍ E. Multiple UAV systems: A survey[J]. Unmanned Systems, 2020, 8(2): 149-169.
[8] FAN B, LI Y, ZHANG R, et al. Review on the technological development and application of UAV systems[J]. Chinese Journal of Electronics, 2020, 29(2): 199-207.
[9] 梁晓龙, 张佳强, 吕娜. 无人机集群[M]. 西安: 西北工业大学出版社, 2018．
[10] 刘立章, 张骞, 赵梓涵. 无人机蜂群拦截系统作战构想与关键技术[J]. 指挥控制与仿真, 2021, 43(1): 7.
[11] 李翔. 无人机集群组网通信方式及其发展趋势探究[J]. 通信电源技术, 2022, 39(16): 106-108.
[12] 吴超宇, 王明珠, 张旭东. 浅谈无人机集群组网通信技术[J]. 信息通信, 2019, 199(7): 128-130.
[13] LI J, CHEN M, DAI F. Prioritizing-based message scheduling for reliable unmanned aerial vehicles ad hoc network[J]. International Journal of Performability Engineering, 2018, 14(9): 2021-2019.
[14] CHEN R, YANG B, ZHANG W. Distributed and collaborative localization for swarming UAVs[J]. IEEE Internet of Things Journal, 2020, 8(6): 5062-5074.
[15] 贾永楠, 田似营, 李擎. 无人机集群研究进展综述[J]. 航空学报, 2020, 41(S1): 4-14.
[16] KORTE B H, VYGEN J, KORTE B, et al. Combinatorial optimization[M]. Berlin: Springer, 2011.
[17] ZHOU Y, RAO B, WANG W. Uav swarm intelligence: Recent advances and future trends[J]. IEEE Access, 2020, 8: 183856-183878.
[18] BOSKOVIC J D, PRASANTH R, MEHRA R K. A multi-layer autonomous intelligent control architecture for unmanned aerial vehicles[J]. Journal of Aerospace Computing, Information, and Communication, 2004, 1(12): 605-628.
[19] AZZOUG Y, BOUKRA A. Enhanced UAV-aided vehicular delay tolerant network (VDTN) routing for urban environment using a bio-inspired approach[J]. Ad Hoc Networks, 2022, 133: 102902.
[20] HAN C, YIN J, YE L. NCAnt: A Network Coding-based Multipath Data Transmission Scheme for Multi-UAV Formation Flying Networks[J]. IEEE Communications Letters, 2021, 25(3): 1041-1044.
[21] SUN G, QIN D, LAN T, et al. Research on clustering routing protocol based on improved PSO in FANET[J]. IEEE Sensors Journal, 2021, 21(23): 27168-27185.
[22] USMAN Q, CHUGHTAI O, NAWAZ N, et al. A reliable link-adaptive position-based routing protocol for flying ad hoc network[J]. Mobile Networks and Applications, 2021, 26(4): 1801-1820.
[23] CHUA M Y K, YU F R, LI J, et al. Medium access control for unmanned aerial vehicle (UAV) ad-hoc networks with full-duplex radios and multipacket reception capability[J]. IEEE Transactions on Vehicular Technology, 2012, 62(1): 390-394.
[24] ALSHBATAT A I, DONG L. Adaptive MAC protocol for UAV communication networks using directional antennas[C]. 2010 International Conference on Networking, Sensing and Control (ICNSC). Chicago, IL, USA: IEEE, 2010: 598-603.
[25] JIANG A, MI Z, DONG C, et al. CF-MAC: A collision-free MAC protocol for UAVs Ad-Hoc networks[C]. 2016 IEEE Wireless Communications and Networking Conference. Doha, Qatar: IEEE, 2016: 1-6.
[26] MOU X, LI H, YAN F, et al. Priority Based Adaptive MAC Protocol for UAV Ad Hoc Networks[C], 2021 7th International Conference on Computer and Communications. Chengdu, China: IEEE, 2021: 210-214.
[27] HUANG N, WU Z. Survey of network reliability evaluation models and algorithms[J]. System Engineering and Electronics, 2013, 35(12): 2651-2660.
[28] LI Y, LI S, DING Q, et al. Research on Reliability of Ad hoc Network Communication in UAVs Formations[C]. 2019 IEEE 6th International Symposium on Electromagnetic Compatibility. Nanjing, China: IEEE, 2019: 1-7.
[29] WANG X, MI Z, WANG H. Performance test and analysis of multi-hop network based on UAV Ad Hoc network experiment[C], 2017 9th International Conference on Wireless Communications and Signal Processing. Nanjing, China: IEEE, 2017: 1-6.
[30] HUSSEN H R, CHOI S C. Performance Analysis of MANET Routing Protocols for UAV Communications[C], 2018 Tenth International Conference on Ubiquitous and Future Networks. Prague, Czech Republic: IEEE, 2018: 70-72.
[31] GRODI R, RAWAT D B, BAJRACHARYA C. Performance evaluation of Unmanned Aerial Vehicle ad hoc networks[C], SoutheastCon 2015. Fort Lauderdale, FL, USA: IEEE, 2015: 1-4.
[32] XU B, LIU T, BAI G. A multistate network approach for reliability evaluation of unmanned swarms by considering information exchange capacity[J]. Reliability Engineering & System Safety, 2022, 219: 108221.
[33] CAO J, LIU C, DONG Z. Effectiveness evaluation of UAV Ad hoc network in complex task environment[C]. 2021 International Conference on Communications, Information System and Computer Engineering. Beijing, China: IEEE, 2021: 745-748.
[34] KO H, KIM B, KONG S H. GNSS multipath-resistant cooperative navigation in urban vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2015, 64(12): 5450-5463.
[35] SU X L, ZHAN X, NIU M, et al. Receiver autonomous integrity monitoring (RAIM) performances of combined GPS/BeiDou/QZSS in urban canyon[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2014, 9(3): 275-281.
[36] YUAN Y, SHEN F, LI X. GPS multipath and NLOS mitigation for relative positioning in urban environments[J]. Aerospace Science and Technology, 2020, 107: 106315.
[37] DUCATELLE F, DI CARO G A, FÖRSTER A, et al. Cooperative navigation in robotic swarms[J]. Swarm Intelligence, 2014, 8: 1-33.
[38] SHARMA R, TAYLOR C. Cooperative navigation of MAVs in GPS denied areas[C]. 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Seoul, Korea: IEEE, 2008: 481-486.
[39] LI J, BI Y, LI K, et al. Accurate 3D localization for MAV swarms by UWB and IMU fusion[C]. 2018 IEEE 14th International Conference on Control and Automation (ICCA). Anchorage, AK, USA: IEEE, 2018: 100-105.
[40] SHANG Y, RUMI W, ZHANG Y, et al. Localization from connectivity in sensor networks[J]. IEEE Transactions on parallel and distributed systems, 2004, 15(11): 961-974.
[41] BORG I, GROENEN P J F. Modern multidimensional scaling: Theory and applications[M]. New York, NY, USA: Springer, 2005.
[42] 马朋, 张福斌, 徐德民. 基于距离量测的双领航多AUV协同定位队形优化分析[J]. 控制与决策, 2018, 33(2): 256-262.
[43] LI J, YANG G, CAI Q, et al. Cooperative navigation for UAVs in GNSS-denied area based on optimized belief propagation[J]. Measurement, 2022, 192: 110797.
[44] SHIZHUANG W, XINGQUN Z, YAWEI Z, et al. Highly reliable relative navigation for multi-UAV formation flight in urban environments[J]. Chinese Journal of Aeronautics, 2021, 34(7): 257-270.
[45] SHEN J, WANG S, ZHAN X. Multi-UAV cluster-based cooperative navigation with fault detection and exclusion capability[J]. Aerospace Science and Technology, 2022, 124: 107570.
[46] FAN Y, QI X, LIU L. Fault-tolerant cooperative localization of 3D Mobile networks via two-layer filter multidimensional scaling[J]. IEEE Sensors Journal, 2020, 21(6): 8354-8366.
[47] 付有斌, 康巧燕, 王建峰等. 无人机飞行自组网通信协议[J]. 指挥与控制学报, 2021, 7(1): 89-96.
[48] 游文静, 董超, 吴启晖. 大规模无人机自组网分层体系架构研究综述[J]. 计算机科学, 2020, 47(9): 226-231.
[49] ZAFAR W, KHAN B M. A reliable, delay bounded and less complex communication protocol for multicluster FANETs[J]. Digital Communications and Networks, 2017, 3(1): 30-38.
[50] YU Y, RU L, FANG K. Bio-Inspired Mobility Prediction Clustering Algorithm for Ad Hoc UAV Networks[J]. Engineering Letters, 2016, 24(3): 83-92.
[51] ZHONG Y, YAO P Y, SUN Y. Research on phased-forming method of manned/unmanned aerial vehicle task coalition[J]. Systems Engineering and Electronics, 2017, 39(9): 2031-2038.
[52] 黄巍, 陈俊良, 李犹海. 无人机自组网技术综述与发展展望[J]. 电讯技术, 2022, 62(1): 138-146.
[53] GUPTA L, JAIN R, VASZKUN G. Survey of important issues in UAV communication networks[J]. IEEE communications surveys & tutorials, 2015, 18(2): 1123-1152.
[54] LAKEW D S, SA’AD U, DAO N N, et al. Routing in flying ad hoc networks: A comprehensive survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(2): 1071-1120.
[55] VASHISHT S, JAIN S, AUJLA G S. MAC protocols for unmanned aerial vehicle ecosystems: Review and challenges[J]. Computer Communications, 2020, 160: 443-463.
[56] HU J, XIE L, LUM K Y, et al. Multiagent information fusion and cooperative control in target search[J]. IEEE Transactions on Control Systems Technology, 2012, 21(4): 1223-1235.
[57] ZHU Q, ZHOU R, ZHANG J. Connectivity maintenance based on multiple relay UAVs selection scheme in cooperative surveillance[J]. Applied Sciences, 2016, 7(1): 8.
[58] COSTA J A, PATWARI N, HERO III A O. Distributed weighted-multidimensional scaling for node localization in sensor networks[J]. ACM Transactions on Sensor Networks (TOSN), 2006, 2(1): 39-64.
[59] ABD AZIZ A, SEKERCIOGLU Y A, FITZPATRICK P, et al. A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks[J]. IEEE communications surveys & tutorials, 2012, 15(1): 121-144.
[60] 邸斌, 周锐, 董卓宁. 考虑信息成功传递概率的多无人机协同目标最优观测与跟踪[J]. 控制与决策, 2016, 31(4): 616-622.
[61] GERASIMOS G. RIGATOS. Distributed filtering over sensor networks for autonomous navigation of UAVs[J]. Intelligent Service Robotics, 2012, 5(3): 179-198.
[62] FU S, ZHANG Y, Ceriotti M. Modeling packet loss rate of IEEE 802.15.4 links in diverse environmental conditions[C]. 2018 IEEE Wireless Communications and Networking Conference. Barcelona, Spain: IEEE, 2018: 1-6.
[63] WANG J, XIN M. Integrated optimal formation control of multiple unmanned aerial vehicles[J]. IEEE Transactions on Control Systems Technology, 2012, 21(5): 1731-1744.
[64] XU B, LIU T, BAI G. A multistate network approach for reliability evaluation of unmanned swarms by considering information exchange capacity[J]. Reliability Engineering & System Safety, 2022, 219: 108221.
[65] NATKANIEC M, KOSEK-SZOTT K, SZOTT S, et al. A survey of medium access mechanisms for providing QoS in ad-hoc networks[J]. IEEE communications surveys & tutorials, 2012, 15(2): 592-620.
[66] SUMAN B, MANGAL L, SHARMA S. Analyzing Impact of TDMA MAC Framing Structure on Network Throughput for Tactical MANET Waveforms[C]. CAC2S 2013. Paris: Atlantis Press, 2013: 1-8.
[67] WANG W J, DONG C, WANG H, et al. Design and Implementation of Adaptive MAC Framework for UAV Ad Hoc Networks[C]. 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks. Washington D. Hefei, China: IEEE, 2016: 195-201.
[68] 杨光, 姚路, 任培. 基于排队论的TDMA数据链报文传输时延分析[J]. 计算机科学, 2014, 41(3): 120-123.
[69] 康建设, 宋文渊, 白永生等. 装备可靠性工程[M]. 北京: 国防工业出版社, 2019．
[70] MALAJNER M, PLANINŠIČ P, GLEICH D. UWB ranging accuracy[C]. 2015 International Conference on Systems, Signals and Image Processing (IWSSIP). London, UK: IEEE, 2015: 61-64.
[71] LIAN SANG C, ADAMS M, HÖRMANN T, et al. Numerical and experimental evaluation of error estimation for two-way ranging methods[J]. Sensors, 2019, 19(3): 616.
[72] FLUERATORU L, WEHRLI S, MAGNO M, et al. High-accuracy ranging and localization with ultrawideband communications for energy-constrained devices[J]. IEEE Internet of Things Journal, 2021, 9(10): 7463-7480.
[73] MIAO C, DAI G, MAO K, et al. RI-MDS: Multidimensional scaling iterative localization algorithm using RSSI in wireless sensor networks[J]. International Journal of Distributed Sensor Networks, 2015, 11(11): 687258.
[74] SUN X, CHEN T, LI W, et al. Perfomance research of improved mds-map algorithm in wireless sensor networks localization[C]. 2012 International Conference on Computer Science and Electronics Engineering. Hangzhou, China: IEEE, 2012, 2: 587-590.
[75] CHEN H, WANG D, YUAN F, et al. A MDS-based localization algorithm for underwater wireless sensor network[C]. 2013 OCEANS-San Diego. San Diego, CA, USA: IEEE, 2013: 1-5.
[76] FORERO P A, GIANNAKIS G B. Sparsity-exploiting robust multidimensional scaling[J]. IEEE Transactions on Signal Processing, 2012, 60(8): 4118-4134.
[77] BLOUVSHTEIN L, COHEN-OR D. Outlier detection for robust multi-dimensional scaling[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 41(9): 2273-2279.
[78] WYMEERSCH H, LIEN J, WIN M Z. Cooperative localization in wireless networks[J]. Proceedings of the IEEE, 2009, 97(2): 427-450.
[79] 卞立新, 罗兴柏, 李金明等. 基于费效分析的武器装备体系结构优选研究[J]. 系统仿真技术, 2016 (2): 102-105.
[80] GU L, XI X, LIU K, et al. Cost effectiveness model and optimization of weapon system based on cost as an independent variable[C]. 2016 12th World Congress on Intelligent Control and Automation (WCICA). Guilin, China: IEEE, 2016: 2455-2459.
[81] 赵曰强, 安实, 麦强等. 装备费用效能分析及建模的方法研究[J]. 系统仿真学报, 2019, 31(8): 1521-1540.