AI 中文总结
研究无人机感知与跟踪问题，提出AUGUST方法，集成条件信道编码与生成解码模块，通过位置和信噪比嵌入及加权训练目标的扩散模型，实现更准确的无人机点云重建与定位，提升对其姿态和形状信息的捕获能力。
AI 中文摘要
本文研究无人机感知与跟踪问题，基站利用天线阵列照射飞行无人机，利用反射回波进行时隙点云成像。基于历史无人机位置先验确定每个时隙成像区域，无人机由含空间信息和电磁特性的电磁点云表示。提出基于阵列的统一生成式无人机感知与跟踪（AUGUST）方法，集成条件信道编码和生成解码模块。编码模块结合位置和信噪比嵌入，解码模块采用带加权训练目标的扩散模型。仿真结果表明，该方法重建点云保真度更高，定位性能优于传统基于模型的基线，能更准确获取无人机姿态和形状信息。
英文摘要
In this paper, we study an unmanned aerial vehicle (UAV) sensing and tracking problem, where a base station equipped with an antenna array continuously illuminates a flying UAV and exploits the reflected echoes for slot-wise point cloud imaging within its potential flight region. To accomplish this task, the imaging region for each slot is determined based on the prior of the historical UAV positions. Then, the UAV is represented by an electromagnetic point cloud in this region that contains its spatial information and electromagnetic properties (EPs), enabling the unified extraction of UAV position, attitude, and shape from the reconstructed point cloud. The EP point cloud imaging for the UAV based on echo signals is a complex inverse problem. To this end, we propose an Array-based Unified Generative UAV Sensing and Tracking (AUGUST) approach, which integrates a conditional channel encoding module and a generative decoding module. The encoding module incorporates position and signal-to-noise ratio embeddings to stabilize the UAV intrinsic feature extraction under fast UAV position and channel variations, and maps the encoded features to a latent space regularized by a learnable flow-based prior. The decoding module employs a diffusion model with a weighted training objective to reconstruct the UAV point cloud guided by the extracted features. The simulation results demonstrate that the reconstructed point clouds via the proposed AUGUST approach present higher fidelity compared to the benchmark schemes, thereby enabling a more accurate capture of the UAV attitude and shape information. The AUGUST approach also presents a substantial gain over the conventional model-based baseline in positioning performance.
Comments16pages, 13figures
