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arXiv 2602.14985eess.SP
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多静态背散射系统中实时距离-角度估计与标签定位

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

Nokia Bell Labs(诺基亚贝尔实验室)

Tara Esmaeilbeig, Kartik Patel, Traian E. Abrudan, John Kimionis, Eleftherios Kampianakis, Michael S. Eggleston

AI总结:

本文提出低复杂度算法JRAC和SRAE,实现多静态背散射系统中实时距离-角度估计与标签定位,实验显示其在定位精度和效率上均优于传统方法。

摘要:

多静态背散射网络(BNs)是6G环境物联网范式中联合通信与定位的强大候选者。在大规模多静态部署中实现实时定位需要高效的算法来估计关键参数,如距离和到达角度(AoA),并融合这些参数以获得位置估计。我们提出两种低复杂度算法,联合距离-角度聚类(JRAC)和分阶段距离-角度估计(SRAE)。两者在距离和角度估计的准确性上与FFT和子空间基线相当,但显著减少了计算量。我们随后引入两种实时定位算法,将估计的距离和AoAs融合:最大似然(ML)方法通过梯度搜索求解,以及迭代加权最小二乘(IRLS)方法。两者在定位准确性上与基于ML的暴力搜索相当,但复杂度低得多。在具有4个照明器、1个多天线接收器和100个标签的真实世界大规模多静态测试台上进行的实验表明,JRAC和SRAE将运行时间减少高达40倍,而IRLS在不降低定位准确性的情况下,比基于ML的暴力搜索减少高达500倍。所提出的方法在子6GHz频段、40 MHz带宽下,所有100个标签的中位定位误差为3米。这些结果表明,多静态距离-角度估计和定位算法可以使实时、可扩展的背散射定位在下一代环境物联网网络中成为可能。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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