Research review on autonomous detect and avoid technologies for unmanned aerial vehicles
Article Text (Baidu Translation)
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摘要: 为推动无人驾驶航空器自主探测与避让系统适配未来低空多元化、高密度、高动态的复杂运行环境，从核心技术原理及标准切入，介绍了系统基本功能架构，剖析了现有标准在复杂低空环境中的适用性与局限性；围绕目标探测、动态跟踪、冲突告警、避让决策四大关键技术，阐述了各技术在国内外的研究成果，从不同维度对比了各技术手段的特性；着眼于未来自主探测与避让系统的发展趋势，探讨了目标探测、跟踪、预警/告警、自主决策等重要环节的潜在发展方向，总结了未来该领域可能面临的挑战与机遇。研究结果表明：目标探测技术手段呈现多样性，但受功耗、质量、频段资源等多重约束，难以满足低空无人机多场景、全自主探测需求；跟踪与告警算法存在架构通用性不足、泛化能力弱及计算资源有限等问题，难以适应复杂动态环境；避让决策仍以基于传统规则或非协作优化为主，在高密度无人机协同场景下决策效率与灵活性显著受限；探测与避让技术作为保障有人/无人混合空域安全运行的核心支撑，其未来发展将呈现四大趋势，即协作与非协作技术融合的多维度监视、多目标感知与智能学习结合的精准跟踪、风险度量与状态推演协同的动态预警、分布式协同的自主决策机制，为后续探测与避让系统研发升级与实际应用提供参考。Abstract: The future complex low-altitude operational environment is characterized by diversity, high density, and high dynamics. To promote autonomous detect and avoid (DAA) systems of unmanned aerial vehicles (UAVs) to adapt to this, the basic functional architecture of the system was introduced based on core technical principles and standards. The applicability and limitations of existing standards in complex low-altitude environments were analyzed. The focus was put on four key technologies including target detection, dynamic tracking, conflict alerting, and avoidance decision-making. Domestic and foreign research achievements for each technology were elucidated. The characteristics of various technical means were compared from different dimensions. The development trends of future autonomous DAA systems were explored. Potential advancements in key processes such as target detection, tracking, early warning/alerting, and autonomous decision-making were discussed. Possible challenges and opportunities in this field were summarized. The research results show that target detection technologies are diverse. However, they are constrained by factors like power consumption, weight, and frequency band resources, making it difficult for them to meet the multi-scenario and fully autonomous detection needs of low-altitude UAVs. Tracking and alerting algorithms suffer from issues, including insufficient architectural versatility, weak generalization ability, and limited computing resources, hindering them from adapting to complex dynamic environments. Avoidance decision-making still relies mainly on traditional rules or non-cooperative optimization, leading to significant limitations in decision-making efficiency and flexibility in high-density UAV collaborative scenarios. In general, DAA technology serves as a core support for ensuring the safe operation of manned/unmanned mixed airspace. Future DAA systems will develop along four major trends, including multi-dimensional surveillance integrating cooperative and non-cooperative technologies, accurate tracking combining multi-target perception and intelligent learning, dynamic early warning with collaborative risk measurement and state deduction, and distributed collaborative autonomous decision-making mechanisms. A reference is then provided for the subsequent research and development, upgrading, and practical application of DAA systems.
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图 4 典型传感设备探测范围和功耗
Figure 4. Detection range and power consumption of typical sensing devices
图 5 无人机多源传感器信息融合级别划分
Figure 5. Classification of information fusion levels for UAV multi-sensor systems
图 7 不同扩容方案下监视容量值
Figure 7. Surveillance capacity values under different capacity expansion schemes
图 8 未来机载自主DAA技术趋势特点
Figure 8. Trends and characteristics of future airborne autonomous DAA technologies
表 1 DAA技术标准适用性对比
Table 1. Applicability comparison of DAA technical standards
标准 适用无人机 适用空域 采用传感器 DAA保护区 避让规则 ISO_DIS_15964 具备探测与避让功能的无人机 所有空域 非协作式雷达，光学传感器 未明确规定 根据不同系统类型(短程/中远程/短中长传感器混合)架构、性能、功能，匹配不同场景响应策略 ASTM F3442-23 最大尺寸(翼展/直径)小于7.62 m，飞行速度小于51.44 m·s-1的无人机 G、E类空域(地面以上约365.76 m高度以下)，B、C、D类空域(离地约121.92~ 152.40 m高度以下)，以及低于障碍物净空表面或设施地图指定海拔的低空区域 协作/非协作式传感器及混合传感器 水平609.6 m、垂直76.2 m(安全间隔边界)；水平152.4 m、垂直30.48 m (NMAC边界) 优先避让最小分离距离(Closest Point of Approach, CPA)时间最短的目标；发生多威胁时，优先避让可能引发近距空中碰撞(Near Mid-air Collision, NMAC) 的目标 RTCA DO-365B 质量大于等于25 kg的中大型无人机 穿越B、C、D、E、G类空域, C、D、E、G类空域(机场视觉交通模式或地面运行的无人机场景除外), 运行高度大于121.92 m 协作/非协作式传感器及混合传感器 水平1 219.20 m、垂直137.16 m(设置213.36 m为交通咨询警告阈值) DAA系统通过定义安全距离，提供告警和引导；无人机接近净空区域(DAA Well Clear, DWC)边界时，执行避让措施 
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表 2 目标探测技术综合对比分析
Table 2. Comprehensive comparative analysis of target detection technologies
关键技术 标准支撑 提供信息 测距能力 全天候 功耗 多目标 成本 协作目标探测 1090ES √ 位置/状态 强 √ 低 √ 低 UAT √ 强 √ 低 √ 低 广播式Remote ID √ 弱 √ 低 √ 低 网络式Remote ID √ 强 √ 低 √ 低 ACAS Xu √ 强 √ 高 √ 高 FLARM √ 强 √ 低 √ 高 飞行自组网 × 弱 × 低 √ 高 非协作目标探测 视觉传感器 × 图像 弱 × 低 √ 低 激光雷达 × 距离 强 × 高 √ 高 毫米波雷达 × 距离/方位/仰角 弱 √ 高 √ 低
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表 4 目标跟踪技术综合对比分析
Table 4. Comprehensive comparative analysis of target tracking technologies
关键技术 能力特征 优点 缺点 基于运动学模型的轨迹跟踪[52-58] 聚焦目标状态跟踪估计 遮挡场景下仍可跟踪，LKF、EKF、UKF、AKF算法计算效率高、实时优化能力强 误差易累积，LKF、EKF、UKF算法对传感器要求较高，粒子滤波和IMM计算复杂程度较高 基于数据驱动的轨迹跟踪 基于模型和数据驱动的混合算法[59] 跟踪控制鲁棒性强，扰动预测网络泛化稳定 依赖高精度辨识参数，预训练与参数调试成本高，计算复杂实时性受限 RNN-enhanced IMM-KF[60] 跟踪精度显著提升，鲁棒性强 高度依赖高精度训练数据，泛化性、实时性受限，计算复杂度高 多数据驱动模型[61] 适配多样跟踪场景，泛化性强 数据依赖强，计算复杂度高，实时性差 平滑器迭代学习控制算法[62] 扰动估计准，收敛快，鲁棒性强 场景局限大，模型依赖重，实时性不足 数据驱动模型预测控制算法[63] 在线更新模型、预测精度高，模型泛化性强 计算复杂度高，数据量需求大 多模态数据融合与跟踪[64] 多源数据互补，避障与跟踪协同 模型迁移性差，实时避障滞后
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表 5 冲突告警技术综合对比分析
Table 5. Comprehensive comparative analysis of conflict alert technologies
关键技术 能力特征 优点 缺点 冲突风险度量 贝叶斯网络模型[66] 整合多源因子，拓扑灵活 复杂度高实时性差，数据依赖强 STP模型[68] 精准定位局部高风险，追踪风险演化趋势 高动态目标告警滞后、多机冲突计算复杂、数据依赖性强 优劣解距离法[69] 动态权重、评估直观性 高飞行量评估滞后，耦合效应处理不足 蒙特卡罗仿真方法[70] 逻辑简便，覆盖极端场景 计算效率低，数据依赖强 支持向量机[72] 非线性分类精准，小样本泛化好 多机场景处理弱，虚警率偏高 冲突告警逻辑 冲突风险判定规则[76] 时间与距离相结判定，为安全上“双保险” 模型适配性不足、阈值依赖经验，存在虚警/漏警风险 告警逻辑判断[7, 77] 等级预警，多层级决策保障运行安全
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表 6 避让决策技术综合对比分析
Table 6. Comprehensive comparative analysis of avoidance decision-making technologies
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