UAV Target Reconstruction and Imaging Algorithm Design and Performance Evaluation Using CSI of ISAC Signal

The increasing consumer application demands of unmanned aerial vehicles (UAVs) in low-altitude airspace lead to many challenges for traditional UAV monitoring in terms of small UAV target sensing probability, target imaging resolution, equipment cost, etc. To solve these problems cost-effectively, the integrated sensing and communication (ISAC) technology based on cellular networks shows potential communication and sensing capabilities by sharing the same hardware equipment. This paper proposes a novel UAV target imaging algorithm using the channel state information (CSI) based ISAC signal to improve the target sensing probability and achieve target imaging. An ISAC signal model is designed to reconstruct the target image from CSI based on the time-frequency transformation relationship between target motion and CSI. Considering the sparsity of target scattering points, a two-dimensional alternating direction method of multipliers joint autofocus (2D-ADMM-AF) algorithm is proposed using compressed sensing theory, which achieves super-resolution reconstruction of the target image while eliminating the high side lobe problem caused by the sparse aperture problem that may exist in the ISAC scene. Further, the image entropy is introduced in the reconstruction process to achieve target translation compensation, eliminating the image blur problem caused by the translational motion of the UAV target. The simulation results show that compared with the traditional algorithm, the image entropy reconstructed by the 2D-ADMM-AF is reduced by 27% and the operating efficiency is improved by 85.5%. In addition, the real data of the DJI M300 UAV is collected using the ISAC testbed to verify the ability to image targets with RCS=0.3m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, 0.7m×0.7m.