该数据集建立于2025年7月，利用Python仿真平台构建生成，主要面向复杂动态场景下的大规模无人集群协同拦截问题，旨在进行多智能体强化学习、动态目标分组、图注意力关系建模和跨场景泛化算法的性能分析与验证。数据集基于二维集群拦截仿真环境生成，构建了己方导弹集群与敌方无人机目标集群之间的对抗交互场景，其中训练场景主要为20枚己方导弹拦截10架敌方无人机，并扩展形成15vs10、10vs10等不同规模，以及训练内策略、训练外策略、不同敌方速度和不同敌方队形等测试配置。数据集包含一个txt文本文件，即r_replay_bufer.txt。r_replay_bufer.txt为无人集群执行任务过程中的状态、动作、奖励等强化学习要素数据，包含4列数据，分别为无人集群状态（state）、无人集群个体动作（action）、团队奖励函数值（reward）、无人集群下一状态（next state）以及当前任务是否完成标志（done）。数据集内容包括智能体状态、动作指令、奖励信号、任务终止标志、目标动态分组结果、敌方小组特征、图注意力权重及任务成功率、平均击杀数、平均步数等评估指标。本数据集可有效支撑无人集群协同拦截策略训练、目标分配机制分析和跨场景泛化性能测试。
", "ds_source": "本数据集为完全仿真生成数据，不源自特定文献、实测或第三方下载。
", "ds_process_way": "本数据集基于二维无人集群协同拦截仿真环境生成。首先初始化己方导弹与敌方无人机的位置、速度、偏航角和健康状态，并对二维交战区域坐标进行归一化处理；随后依据双方动力学模型逐步更新状态，并根据攻击距离、攻击角和毁伤概率判断拦截结果，记录状态、动作、奖励、终止标志和任务结果等轨迹信息。同时，采用K-means算法对敌方目标进行周期性动态分组，提取小组规模、中心位置、边界半径和平均速度等特征，并利用图注意力机制计算敌方小组重要性权重。最后，结合不同对抗规模、敌方策略、速度和队形设置生成跨场景样本，并统计任务成功率、平均击杀数和平均步数等评价指标。
", "ds_quality": "该数据呈现了无人集群跨场景协同拦截多目标的详细数据。
", "ds_acq_start_time": "2025-01-01 00:00:00", "ds_acq_end_time": null, "ds_acq_place": "合肥", "ds_acq_lon_east": 117.43, "ds_acq_lat_south": 31.57, "ds_acq_lon_west": 117.07, "ds_acq_lat_north": 31.97, "ds_acq_alt_low": 4.0, "ds_acq_alt_high": 80.0, "ds_share_type": "open-access", "ds_total_size": 737163096, "ds_files_count": 0, "ds_format": ".txt", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "3f9e8eaf-d0d7-4bfd-8545-67972b256f8d.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": "", "organization_id": "9ecaaa78-39e9-411e-9f24-274e12aa643f", "ds_serv_man": "虞文武", "ds_serv_phone": "15051861330", "ds_serv_mail": "wwyu@seu.edu.cn", "doi_value": "", "subject_codes": [ "410" ], "quality_level": 0, "publish_time": "2026-07-09 10:58:48", "last_updated": "2026-07-09 10:58:48", "protected": false, "protected_to": "2028-06-30 00:00:00", "lang": "zh", "cstr": "", "i18n": { "en": { "title": "Dataset for Cross-Scenario Collaborative Interception by Unmanned Swarms", "ds_format": ".txt", "ds_source": "This dataset is completely synthetically generated through simulation and does not originate from specific literature, field measurements, or third-party downloads.", "ds_quality": "This dataset presents detailed data on the cross-scenario collaborative interception of multiple targets by unmanned swarms.", "ds_ref_way": "", "ds_abstract": "This dataset was established in July 2025 and generated using a Python simulation platform. It is primarily designed for the large-scale unmanned swarm cooperative interception problem in complex dynamic scenarios, aiming to support performance analysis and validation of multi-agent reinforcement learning, dynamic target grouping, graph attention relationship modeling, and cross‑scenario generalization algorithms. The dataset is generated from a 2D swarm interception simulation environment, which constructs adversarial interaction scenarios between a friendly missile swarm and an enemy UAV swarm. The training scenarios mainly involve 20 friendly missiles intercepting 10 enemy UAVs, with extensions to different scales such as 15vs10 and 10vs10, as well as test configurations including in‑training policies, out‑of‑training policies, different enemy speeds, and different enemy formations. The dataset contains a single TXT text file, namely r_replay_bufer.txt. This file records the reinforcement learning elements—states, actions, rewards, etc.—during the unmanned swarm's mission execution. It contains four columns of data, namely the swarm state, individual swarm actions, the team reward function value, the next swarm state, and a flag indicating whether the current task is completed (done). The data content includes agent states, action commands, reward signals, task termination flags, dynamic target grouping results, enemy subgroup characteristics, graph attention weights, and evaluation metrics such as task success rate, average number of kills, and average number of steps. This dataset can effectively support training of cooperative interception policies for unmanned swarms, analysis of target assignment mechanisms, and testing of cross‑scenario generalization performance.", "ds_time_res": "", "ds_acq_place": "Hefei", "ds_space_res": "", "ds_projection": "", "ds_process_way": "This dataset is generated based on a 2D simulation environment for cooperative interception by a UAV swarm. First, the positions, velocities, yaw angles, and health statuses of friendly missiles and enemy UAVs are initialized, and the coordinates of the 2D engagement area are normalized. Subsequently, the states are updated iteratively according to the respective dynamic models; interception outcomes are determined based on attack distance, attack angle, and kill probability, while trajectory data—including states, actions, rewards, termination flags, and mission results—are recorded. Concurrently, the K-means algorithm is employed to dynamically group enemy targets at regular intervals, extracting features such as group size, centroid location, boundary radius, and average velocity, while a graph attention mechanism is used to calculate importance weights for the enemy groups. Finally, cross-scenario samples are generated by varying parameters such as engagement scale, enemy strategy, velocity, and formation, and performance metrics—including mission success rate, average number of kills, and average number of steps—are calculated.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "协同拦截", "多智能体强化学习" ], "ds_subject_tags": [ "工程与技术科学基础学科" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "ds_contributors": [ { "true_name": "虞文武", "email": "wwyu@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "虞文武", "email": "wwyu@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "虞文武", "email": "wwyu@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" } ], "category": "其他" }