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

Zhuoyang Liu, Yixiang Luomei, Feng Xu

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.

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

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.
Paper Structure (21 sections, 19 equations, 13 figures, 4 tables)

This paper contains 21 sections, 19 equations, 13 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motivation and Contribution
  4. Organization and Notation
  5. System Model
  6. Sensing principles of ISAC systems
  7. ISAC-based mDM Architecture
  8. Problem formulation
  9. mDD Estimation Solution in ISAC-based mDM System
  10. Clutter-Target Feature Model
  11. LTM Network Design
  12. Phase Unwrapping via CNN-Based Method
  13. Signal Decoupling via Learnable Template
  14. Network Architecture and Loss Function Design
  15. Numerical Evaluations
  16. ...and 6 more sections

Figures (13)

  • Figure 1: ISAC application in smart city.
  • Figure 2: The entire operation of the ISAC system, involves strategies for both the transmit communication modulation and the sensing signal modulation.
  • Figure 3: The geometry of the ISAC-based mDM system.
  • Figure 4: LTM Architecture: The architecture consists of two main components. The top block represents the workflow for the CNN-based phase unwrapping, which addresses the phase-wrapping issues in the extracted signals. The bottom block illustrates the LTM network, specifically designed for decoupling the mDD signal from clutter interference.
  • Figure 5: LTM-based Neural Network for mDD estimation.
  • ...and 8 more figures