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

TL;DR

The paper tackles real-time tag localization in large-scale multi-static backscatter networks by introducing two low‑complexity joint range–angle estimators, JRAC and SRAE, and two real-time fusion-based localization methods, ML gradient ascent with line search and IRLS. JRAC and SRAE efficiently construct and interpret range–angle representations to extract bistatic range and AoA with far lower computational burden than traditional 2D FFT/MUSIC baselines, enabling scalable operation. For localization, ML and IRLS fuse the estimated ranges and angles, with IRLS augmented by a scaling/projection scheme to mitigate outliers, achieving accuracy on par with brute-force ML while dramatically reducing complexity. Real-world experiments on a 4-TX/1-RX, 100-tag testbed demonstrate up to 40× runtime reductions for estimation and up to 500× for ML-based localization, achieving a 3 m median positioning error across 100 tags in sub-6 GHz operation, highlighting the practicality of real-time, scalable ambient IoT localization in multi-static BN.

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.

Figures (6)

• Figure 1: An illustration of a multi-static BN comprising 4 TXs, 1 multi-antenna RX, and a frequency-shifting tag: The tag shifts the carrier signal to an adjacent band to allow the RX process both signals simultaneously, and enable the bistatic range-angle estimation without external synchronization. Combining range-angle estimates from multiple TX-RX-pairs enable tag localization.
• Figure 2: Elliptical positioning scenario with bistatic range measurements from four TXs and one RX. The tag’s location is determined as the intersection of the four ellipses, each defined by a TX–RX pair as its foci and the hyperplane from the estimated tag AoA.
• Figure 3: (a)-(b) Range and angle estimation errors using simulation data: Range-AoA estimation methods have higher ranging accuracy compared to the range-only method IR-First. Joint methods such as JRAC achieves higher ranging and angle estimation accuracy compared to the stage-wise method, SRAE. (c) Using AoA for localization with various number of TXs: We compare positioning accuracy using range only and joint range-angle measurements from the synthetic data. We consider SNR$=5$ dB, $L_1=L_2=3$ scatterers, 1D MUSIC for the range estimation and 2D MUSIC for joint range and angle estimation. Localization uses ML-based Gradient Ascent with Line Search.
• Figure 4: The experimental setup of a large-scale BN consisting of 4 TXs, 1 RX with 4-element antenna array, and 100 tags in an enclosed lab. The walls with metal surface and the ground introduce significant multipath in the channel.
• Figure 5: Schematic of the experimental setup consisting 4 TXs, 1 RX with 4-element antenna array, and 100 tags. The positive-X direction in the antenna frame denotes the broadside direction of the receiver antenna array.

Theorems & Definitions (2)

• Remark 1
• Remark 2