Clutter-Aware Integrated Sensing and Communication: Models, Methods, and Future Directions

TL;DR

The paper tackles clutter as a fundamental bottleneck in wideband ISAC, proposing a unified wideband MIMO-OFDM model that captures cold and hot clutter across space, time, and frequency. It develops comprehensive clutter representations (amplitude statistics, SIRV models, structured covariances), and advances receiver-side suppression methods (slow-time processing, spatial filtering, STAP/SFTAP) while enabling proactive transceiver co-design under QoS constraints. Key contributions include waveform-aware inner clutter kernels, waveform- and dimension-aware covariance learning, and multi-domain optimization frameworks (EM/network) that leverage priors and learning-based aids to mitigate clutter in dynamic environments. The work charts future directions in robust online clutter handling, multipath exploitation, digital twins, new waveform designs, and standardization to enable environment-adaptive, clutter-resilient ISAC in 6G+ networks.

Abstract

Integrated sensing and communication (ISAC) can substantially improve spectral, hardware, and energy efficiency by unifying radar sensing and data communications. In wideband and scattering-rich environments, clutter often dominates weak target reflections and becomes a fundamental bottleneck for reliable sensing. Practical ISAC clutter includes "cold" clutter arising from environmental backscatter of the probing waveform, and "hot" clutter induced by external interference and reflections from the environment whose statistics can vary rapidly over time. In this article, we develop a unified wideband multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) signal model that captures both clutter types across the space, time, and frequency domains. Building on this model, we review clutter characterization at multiple levels, including amplitude statistics, robust spherically invariant random vector (SIRV) modeling, and structured covariance representations suitable for limited-snapshot regimes. We then summarize receiver-side suppression methods in the temporal and spatial domains, together with extensions to space-time adaptive processing (STAP) and space-frequency-time adaptive processing (SFTAP), and we provide guidance on selecting techniques under different waveform and interference conditions. To move beyond reactive suppression, we discuss clutter-aware transceiver co-design that couples beamforming and waveform optimization with practical communication quality-of-service (QoS) constraints to enable proactive clutter avoidance. We conclude with open challenges and research directions toward environment-adaptive and clutter-resilient ISAC for next-generation networks.

Figures (12)

• Figure 1: Organization of the paper.
• Figure 2: RDMs with and without waveform de-randomization using reciprocal filtering (rectangle: weak target of interest, ellipse: strong target, and $\text{SCNR} = -47.4$ dB).
• Figure 3: Representative ISAC environments.
• Figure 4: Spatial pseudo-spectra and RDMs before and after slow-time filtering in the cold-clutter-only case (no external emitter). The target of interest is indicated by the green dashed line and rectangle, and the strong UAV targets are indicated by the black dashed lines and ellipses. $\text{SCNR} = -45.9$ dB.
• Figure 5: Spatial pseudo-spectra and RDMs before and after slow-time filtering with both cold and hot clutter. The target of interest is marked by the green dashed line and rectangle, the strong UAV targets are marked by the black dashed lines and ellipses, and the external emitter is marked by the purple dashed line. $\text{SCNR} = -47.4$ dB.

Theorems & Definitions (4)

• Remark 1: Extensions to bistatic and multistatic scenarios
• Remark 2: Pilot-only probing
• Remark 3: Application to bistatic/multistatic sensing
• Remark 4: Spatial vs. space-time snapshots