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

TL;DR

This paper introduces a conditional diffusion model for UAV imaging that reconstructs high-fidelity electromagnetic point clouds from reflected signals, enabling accurate UAV position, attitude, and shape sensing.

Contribution

It proposes a novel generative sensing approach using a diffusion model to improve UAV imaging accuracy from wireless echoes.

Findings

Reconstructed point clouds show higher fidelity than existing methods.

The approach enables more accurate UAV attitude and shape estimation.

Simulation results confirm improved position accuracy.

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.