Conditional Diffusion-Based Point Cloud Imaging for UAV Position and Attitude Sensing

Abstract

This paper studies an unmanned aerial vehicle (UAV) position and attitude sensing problem, where a base station equipped with an antenna array transmits signals to a predetermined potential flight region of a flying UAV, and exploits the reflected echoes for wireless imaging. 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. To accomplish this task, we develop a generative UAV sensing approach. The position and signal-to-noise ratio embedding are adopted to assist the UAV features extraction from the estimated sensing channel under the measurement noise and channel variations. Guided by the obtained features, a conditional diffusion model is utilized to generate the point cloud. The simulation results demonstrate that the reconstructed point clouds via the proposed approach present higher fidelity compared to the competing schemes, thereby enabling a more accurate capture of the UAV attitude and shape information, as well as a more precise position estimation.

Figures (5)

• Figure 1: The considered UAV sensing system.
• Figure 2: The illustration of the proposed approach.
• Figure 3: (a)-(b): Potential region view of the point cloud reconstruction result with the relative permittivity based on the proposed approach; (c)-(f): Centralized view of the point cloud reconstruction results with the conductivity based on different approaches; $\text{SNR}$ is fixed as 20 dB.
• Figure 4: The WD comparison under varying SNR conditions.
• Figure 5: The MPE/MDE comparisons under varying SNR conditions.