Learnable Template Matching Approach for Micro-Deformation Monitoring based on Integrated Sensing and Communication Platform

TL;DR

This paper introduces a learnable template-matching method enhanced with AI to improve micro-deformation monitoring accuracy in integrated sensing and communication platforms by effectively removing environmental clutter.

Contribution

The paper presents a novel AI-assisted, learnable template-matching approach that significantly enhances sensing quality and micro-deformation displacement estimation in cluttered urban environments.

Findings

Improved convergence speed and accuracy in micro-deformation displacement prediction.

Effective clutter suppression in integrated sensing and communication systems.

Enhanced environment clutter separation and measurement precision.

Abstract

Existing integrated sensing and communication (ISAC) platforms fail to fully utilize the shared spectrum and aperture resources for sensing, resulting in poor sensing performance. Specifically, weak target sensing on the ISAC platform, such as micro-deformation monitoring (mDM), suffers from inaccurate measurements due to poor sensing quality. In this paper, we propose an AI-assisted approach to alleviate the effect of poor sensing quality in the ISAC system by effectively removing the clutter. We begin by modeling the environment clutter model as a combination of the deterministic and stochastic signals to represent urban coverage scenarios around the base station (BS). A clutter suppression optimization problem is formulated to extract the micro-deformation displacement (mDD) from the original ISAC signals. We then propose a learnable template-matching (LTM) approach to mitigate the influences of clutters, thereby enhancing sensing quality. In particular, the electromagnetic (EM) signal feature of the mDD is embedded into the network to strengthen the mDM signal, and clutter filters are incorporated to suppress environmental clutter. Numerical results illustrate the superiority of our proposed approach concerning convergence speed and accuracy in mDD prediction. By deploying our approach to the BS measurement, the simulation-only trained LTM exhibits impressive performance in environment clutter separation and mDD estimation.