Aspect angle sequences of ballistic conical targets.

<b>NOTE: </b>This dataset is part of the project IMD. We highly recommend that you first review the project details to gain a better understanding of the dataset's background.INTROIn this dataset, you can find the aspect angle sequences for four types of ballistic conical targets. The aspect angle is the angle between the radar line of sight (LOS) and the target's body axis. By combining static electric field data with interpolation methods, an approximate radar echo can be obtained. This method has already been applied in numerous related studies, such as:X. Tian, X. Bai, R. Xue, R. Qin and F. Zhou, "Fusion Recognition of Space Targets With Micromotion," in <i>IEEE Transactions on Aerospace and Electronic Systems</i>, vol. 58, no. 4, pp. 3116-3125, Aug. 2022, doi: 10.1109/TAES.2022.3145303.X. Tian, X. Bai and F. Zhou, "Recognition of Micro-Motion Space Targets Based on Attention-Augmented Cross-Modal Feature Fusion Recognition Network," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-9, 2023, Art no. 5104909, doi: 10.1109/TGRS.2023.3275991.Lee J I, Kim N, Min S, et al. Space target classification improvement by generating micro-Doppler signatures considering incident angle[J]. Sensors, 2022, 22(4): 1653.J. Dong, Q. She and F. Hou, "HRPnet: High-Dimensional Feature Mapping for Radar Space Target Recognition," in <i>IEEE Sensors Journal</i>, vol. 24, no. 7, pp. 11743-11758, 1 April1, 2024, doi: 10.1109/JSEN.2024.3361926.In our simulation, we follow basic physical principles to construct a simplified ballistic model, which is therefore not entirely accurate. The core objective of this project is to build a unified dataset to provide a benchmark for related research, rather than achieving precise modeling. HOWTOThe relationship between this data and the final dynamic echo data is well explained in the paper found at this link. The specific simulation methods, including kinematic modeling and dynamic sequence generation codes, can be found in the GitHub repository for now. Once the final proofreading is completed, they will be added under this project. Repository link: https://github.com/NilasZ/MRM.For any questions, feel free to contact us:[e-mail]@hdr.mq.edu.au<br><br>**注意：** 本数据集属于IMD项目。我们强烈建议您先查阅项目详情，以更好地理解本数据集的背景。 数据集简介：本数据集包含四类锥形弹道目标的姿态角（aspect angle）序列。姿态角指雷达视线（radar line of sight, LOS）与目标机体轴之间的夹角。通过将静电场数据与插值方法相结合，可得到近似的雷达回波。该方法已在诸多相关研究中得到应用，例如： 1. 田曦、白翔、薛然、秦然与周峰，“带微动的空间目标融合识别”，载于*IEEE Transactions on Aerospace and Electronic Systems*，第58卷第4期，第3116-3125页，2022年8月，DOI: 10.1109/TAES.2022.3145303。 2. 田曦、白翔与周峰，“基于注意力增强跨模态特征融合识别网络的微动空间目标识别”，载于*IEEE Transactions on Geoscience and Remote Sensing*，第61卷，第1-9页，2023年，文章编号5104909，DOI: 10.1109/TGRS.2023.3275991。 3. Lee J I, Kim N, Min S, 等. 考虑入射角的微多普勒特征生成对空间目标分类性能的提升[J]. *Sensors*, 2022, 22(4): 1653。 4. 董健、佘清与侯峰，“HRPnet：面向雷达空间目标识别的高维特征映射网络”，载于*IEEE Sensors Journal*，第24卷第7期，第11743-11758页，2024年4月1日，DOI: 10.1109/JSEN.2024.3361926。 在本仿真实验中，我们遵循基本物理原理构建了简化的弹道模型，因此该模型并非完全精确。本项目的核心目标是构建统一的数据集，为相关研究提供基准测试平台，而非实现精准建模。 使用说明：本数据集与最终动态回波数据之间的关联已在该链接对应的论文中得到详细阐释。具体的仿真方法（包括运动学建模与动态序列生成代码）目前可在GitHub仓库中获取。待最终校对完成后，相关内容将被添加至本项目下。仓库链接：https://github.com/NilasZ/MRM。 如有任何疑问，欢迎联系我们：[e-mail]、[e-mail]