From Partial Calibration to Full Potential: A Two-Stage Sparse DOA Estimation for Incoherently-Distributed Sources with Partly-Calibrated Arrays

He Xu; Tuo Wu; Wei Liu; Maged Elkashlan; Naofal Al-Dhahir; Merouane Debbah; Chau Yuen; Hing Cheung So

doi:10.1109/JIOT.2026.3662206

From Partial Calibration to Full Potential: A Two-Stage Sparse DOA Estimation for Incoherently-Distributed Sources with Partly-Calibrated Arrays

He Xu, Tuo Wu^*, Wei Liu, Maged Elkashlan, Naofal Al-Dhahir, Merouane Debbah, Chau Yuen, Hing Cheung So

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is crucial for Industrial Internet of Things (IIoT) applications operating in complex multipath environments, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in cost-sensitive antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method achieves superior estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical industrial environments.

© 2026 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.

Original language	English
Number of pages	4
Journal	IEEE Internet of Things Journal
DOIs	https://doi.org/10.1109/JIOT.2026.3662206
Publication status	Online published - 6 Feb 2026

Funding

This work was partially supported by the Zhejiang Provincial Natural Science Foundation of China under Grant LQN26F010011. (Corresponding author: Tuo Wu.) H. Xu is with the School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China (E-mail: [email protected]). T. Wu and C. Yuen are with the School of Electrical and Electronic Engineering, Nanyang Technological University 639798, Singapore (E-mail: {tuo.wu, chau.yuen}@ntu.edu.sg). W. Liu is with the Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong (E-mail: [email protected]). M. Elkash-lan is with the School of Electronic Engineering and Computer Science at Queen Mary University of London, London E1 4NS, U.K. (E-mail: [email protected]). Naofal Al-Dhahir is with the Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080 USA (E-mail: [email protected]). M. Debbah is with KU 6G Research Center, Department of Computer and Information Engineering, Khalifa University, Abu Dhabi 127788, UAE (E-mail: [email protected]). H. C. So is with the Department of Electrical Engineering City University of Hong Kong, Hong Kong, China. (E-mail: [email protected]).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Research Keywords

Direction-of-arrival (DOA) estimation
Industrial Internet of Things (IIoT)

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title = "From Partial Calibration to Full Potential: A Two-Stage Sparse DOA Estimation for Incoherently-Distributed Sources with Partly-Calibrated Arrays",

abstract = "Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is crucial for Industrial Internet of Things (IIoT) applications operating in complex multipath environments, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in cost-sensitive antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method achieves superior estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical industrial environments.{\textcopyright} 2026 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission. ",

keywords = "Direction-of-arrival (DOA) estimation, Industrial Internet of Things (IIoT)",

author = "He Xu and Tuo Wu and Wei Liu and Maged Elkashlan and Naofal Al-Dhahir and Merouane Debbah and Chau Yuen and So, {Hing Cheung}",

year = "2026",

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T2 - A Two-Stage Sparse DOA Estimation for Incoherently-Distributed Sources with Partly-Calibrated Arrays

AU - Xu, He

AU - Wu, Tuo

AU - Liu, Wei

AU - Elkashlan, Maged

AU - Al-Dhahir, Naofal

AU - Debbah, Merouane

AU - Yuen, Chau

AU - So, Hing Cheung

PY - 2026/2/6

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N2 - Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is crucial for Industrial Internet of Things (IIoT) applications operating in complex multipath environments, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in cost-sensitive antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method achieves superior estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical industrial environments.© 2026 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.

AB - Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is crucial for Industrial Internet of Things (IIoT) applications operating in complex multipath environments, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in cost-sensitive antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method achieves superior estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical industrial environments.© 2026 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.

KW - Direction-of-arrival (DOA) estimation

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DO - 10.1109/JIOT.2026.3662206

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JF - IEEE Internet of Things Journal

ER -

From Partial Calibration to Full Potential: A Two-Stage Sparse DOA Estimation for Incoherently-Distributed Sources with Partly-Calibrated Arrays

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