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

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.

💡 Research Summary

The paper addresses the critical challenge of sensing low‑altitude, small, and slow unmanned aerial vehicles (UAVs) in complex environments such as urban canyons and mountainous terrain. In such scenarios, strong and often dynamic clutter generated by buildings, trees, and moving scatterers severely degrades the performance of integrated sensing and communication (ISAC) systems that rely on orthogonal frequency‑division multiplexing (OFDM). Traditional clutter suppression techniques—primarily moving‑target indication (MTI) and moving‑target detection (MTD) filters that exploit Doppler separation—are ineffective when the Doppler of clutter is close to that of the target or when the clutter itself is non‑static.

To overcome these limitations, the authors introduce a novel type of channel knowledge map (CKM) called the Clutter Angle Map (CLAM). CLAM is a site‑specific database that stores the primary clutter directions (azimuth ϕ and zenith θ) for each location within the coverage area of an ISAC base station (BS). By fusing the user equipment’s (UE) real‑time location information with CLAM, the BS can retrieve the expected clutter angles associated with that UE position.

The system model assumes an uplink bi‑static ISAC configuration where the BS is equipped with a uniform planar array (UPA) of Mx × Mz antennas. The received signal consists of a sum of L dominant clutter paths and S UAV target paths, each characterized by a channel vector h = β α(ϕ,θ), a propagation delay τ, and a Doppler shift f_D. The goal is to estimate the direction‑of‑arrival (DoA), delay, and Doppler of each target while suppressing the clutter contributions.

Spatial‑domain suppression using CLAM
The first contribution is a CLAM‑enabled spatial‑domain clutter suppression method. For each retrieved clutter angle, the corresponding array response vector α(ϕ_l,θ_l) is assembled into a clutter steering matrix A. A zero‑forcing (ZF) projection matrix Z = I − A(A^HA)^{-1}A^H is then applied to the received vector y, effectively nulling the clutter subspace. This operation dramatically improves the signal‑to‑clutter ratio (SCR) before any Doppler or range processing, thereby preserving the weak UAV echoes.

Joint spatial‑Doppler suppression for close‑angle scenarios
When a target’s direction is nearly identical to that of a clutter source, pure spatial nulling cannot separate the two. To address this, the authors propose a two‑step joint spatial‑Doppler algorithm. After the initial CLAM‑based spatial ZF, the residual signal is transformed into the delay‑Doppler domain via OFDM demodulation and 2‑D Fourier processing. All angles other than those identified as potential target directions are treated as “virtual clutter” and are again projected out using ZF. Simultaneously, a high‑pass Doppler filter (MTI‑style) is applied to suppress any remaining low‑Doppler clutter. This combined approach isolates the target’s energy into a specific delay‑Doppler cell while eliminating angularly close clutter, mitigating angle‑coupling effects.

Performance evaluation
Extensive Monte‑Carlo simulations are conducted in a 3‑D low‑altitude scenario featuring both static and dynamic clutter, fast‑moving (≈30 m/s) and slow‑moving (≈3 m/s) UAVs, and varying numbers of clutter sources. Four configurations are compared: (1) conventional MTI, (2) pure spatial ZF without CLAM, (3) CLAM‑based spatial ZF, and (4) the proposed CLAM‑enabled joint spatial‑Doppler algorithm. Key metrics include SCR, DoA error, range error, and velocity RMSE. Results show that the joint algorithm improves SCR by up to 12 dB relative to MTI, reduces DoA error to below 1.2°, and cuts range/velocity RMSE for slow UAVs by more than 60 %. Even with dynamic clutter exhibiting non‑zero Doppler, the algorithm maintains clutter residuals below 10 % of the original power.

Practical considerations
The authors discuss the overhead of building and maintaining CLAM. In a 6G‑era network with dense BS deployments, location information can be exchanged frequently, and CLAM entries (tens of angles per cell) occupy only a few tens of kilobytes, making real‑time updates feasible. They also note that CLAM extends beyond traditional 2‑D clutter intensity maps by providing directional priors, which are especially valuable for bi‑static or multi‑static ISAC configurations where multiple nodes must share clutter knowledge.

Conclusions and future work
The paper establishes CKM‑driven clutter suppression as a new paradigm for low‑altitude ISAC. By leveraging a site‑specific clutter angle map, the proposed method overcomes the Doppler‑based limitations of classic MTI/MTD, achieves robust suppression of both static and dynamic clutter, and enables accurate detection and parameter estimation of slow‑moving UAVs. Future directions include multi‑BS CLAM sharing, field trials in real urban environments, and AI‑based automatic CLAM generation and adaptation.