Real-time Range-Angle Estimation and Tag Localization for Multi-static Backscatter Systems

TL;DR

This paper introduces efficient algorithms for real-time range and angle estimation, and localization in large-scale multi-static backscatter IoT networks, achieving high accuracy with significantly reduced computation.

Contribution

The paper proposes two low-complexity algorithms for range-angle estimation and two fusion methods for localization, enabling real-time processing in large multi-static backscatter systems.

Findings

Achieves median 3m localization error for 100 tags

Reduces runtime by up to 40X with JRAC and SRAE

IRLS method reduces complexity by 500X compared to brute force

Abstract

Multi-static backscatter networks (BNs) are strong candidates for joint communication and localization in the ambient IoT paradigm for 6G. Enabling real-time localization in large-scale multi-static deployments with thousands of devices require highly efficient algorithms for estimating key parameters such as range and angle of arrival (AoA), and for fusing these parameters into location estimates. We propose two low-complexity algorithms, Joint Range-Angle Clustering (JRAC) and Stage-wise Range-Angle Estimation (SRAE). Both deliver range and angle estimation accuracy comparable to FFT- and subspace-based baselines while significantly reducing the computation. We then introduce two real-time localization algorithms that fuse the estimated ranges and AoAs: a maximum-likelihood (ML) method solved via gradient search and an iterative re-weighted least squares (IRLS) method. Both achieve localization accuracy comparable to ML-based brute force search albeit with far lower complexity. Experiments on a real-world large-scale multi-static testbed with 4 illuminators, 1 multi-antenna receiver, and 100 tags show that JRAC and SRAE reduce runtime by up to 40X and IRLS achieves up to 500X reduction over ML-based brute force search without degrading localization accuracy. The proposed methods achieve 3 m median localization error across all 100 tags in a sub-6GHz band with 40 MHz bandwidth. These results demonstrate that multi-static range-angle estimation and localization algorithms can make real-time, scalable backscatter localization practical for next-generation ambient IoT networks.