CKM-Enabled Joint Spatial-Doppler Domain Clutter Suppression for Low-Altitude UAV ISAC

TL;DR

This paper introduces a CKM-based clutter suppression method using CLAM for OFDM ISAC systems, significantly improving sensing accuracy of low-altitude UAVs in complex environments.

Contribution

It proposes a novel CLAM-based clutter suppression technique and a joint spatial-Doppler domain algorithm for enhanced UAV target sensing.

Findings

Effective clutter suppression demonstrated in simulations.

Enhanced accuracy in low-altitude UAV target parameter estimation.

Significant performance improvement over traditional methods.

Abstract

The rapid development of low-altitude economy has placed higher demands on the sensing of small-sized unmanned aerial vehicle (UAV) targets. However, the complex and dynamic low-altitude environment, like the urban and mountainous areas, makes clutter a significant factor affecting the sensing performance. Traditional clutter suppression methods based on Doppler difference or signal strength are inadequate for scenarios with dynamic clutter and slow-moving targets like low-altitude UAVs. In this paper, motivated by the concept of channel knowledge map (CKM), we propose a novel clutter suppression technique for orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) system, by leveraging a new type of CKM named clutter angle map (CLAM). CLAM is a site-specific database, containing location-specific primary clutter angles for the coverage area of the ISAC base station (BS). With CLAM, the sensing signal components corresponding to the clutter environment can be effectively removed before target detection and parameter estimation, which greatly enhances the sensing performance. Besides, to take into account the scenarios when the targets and clutters are in close directions so that pure CLAM-based spatial domain clutter suppression is no longer effective, we further propose a two-step CLAM-enabled joint spatial-Doppler domain clutter suppression algorithm. Simulation results demonstrate that the proposed technique effectively suppresses clutter and enhances target sensing performance, achieving accurate parameter estimation for sensing slow-moving low-altitude UAV targets.