Cooperative Multi-Static Target Localization for ISAC in Cluttered Industrial IoT Networks

TL;DR

This paper introduces a novel ISAC framework for collaborative multi-static target localization in cluttered IIoT environments, employing advanced clutter suppression, adaptive node selection, and reliable data fusion to achieve high-precision results.

Contribution

It presents a new integrated sensing approach with a lightweight clutter suppression and adaptive node selection, improving localization accuracy in dense industrial IoT settings.

Findings

Localization RMSE reduced to about 45 cm after six iterations

Method outperforms benchmarks under the same sensing-resource budget

Rapid convergence within six sensing iterations

Abstract

In this paper, we propose a novel integrated sensing and communications (ISAC) framework for collaborative multi-static target localization in dense Industrial Internet-of-Things (IIoT) environments in the presence of environmental clutter. We first develop a lightweight temporal clutter-suppression learning method to mitigate persistent reflections. Building on this, we propose an iterative localization algorithm that integrates two key components introduced in this work: a sampling-based field-of-view-aware initialization (SFI) scheme and an empirical position error bound (PEB) scheme, which together adaptively identify the most informative subset of sensing nodes. A reliability-aware weighted least-squares estimator is then employed to fuse range and angle-of-arrival measurements from the selected sensing receivers for target localization. Numerical results demonstrate rapid convergence of the proposed method, reducing the localization RMSE by nearly two orders of magnitude within six sensing iterations to about 45 cm, while significantly outperforming all considered benchmarks under the same sensing-resource budget.