3D Localization Method with Uniform Circular Array Driven by Complex Subspace Neural Network

JIANG Wei; ZHI Boxin; YANG Junjie; WANG hui; DING Pengfei; ZHANG Zheng

doi:10.11999/JEIT250395

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JIANG Wei, ZHI Boxin, YANG Junjie, WANG hui, DING Pengfei, ZHANG Zheng. 3D Localization Method with Uniform Circular Array Driven by Complex Subspace Neural Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250395

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JIANG Wei, ZHI Boxin, YANG Junjie, WANG hui, DING Pengfei, ZHANG Zheng. 3D Localization Method with Uniform Circular Array Driven by Complex Subspace Neural Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250395

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3D Localization Method with Uniform Circular Array Driven by Complex Subspace Neural Network

doi: 10.11999/JEIT250395 cstr: 32379.14.JEIT250395

1.
College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China
2.
School of Electronic and Information Engineering, Shanghai Dianji University, Shanghai 201306, China

Funds: The National Natural Science Foundation of China (61202369, 61401269)

Received Date: 2025-05-09
Accepted Date: 2025-11-03
Rev Recd Date: 2025-10-11

Available Online: 2025-11-14

Abstract

Abstract

Objective High-precision indoor localization is increasingly required in intelligent service scenarios, yet existing techniques continue to face difficulties in complex environments where signal frequency offset, multipath propagation, and noise interfere with accuracy. To address these limitations, a 3D localization method using a Uniform Circular Array (UCA) driven by a Complex Subspace Neural Network (CSNN) is proposed to improve accuracy and robustness under challenging conditions. Methods The proposed method establishes a complete localization pipeline based on a hierarchical signal processing framework that includes frequency offset compensation, two-dimensional angle estimation, and spatial mapping (Fig. 2). A dual-estimation frequency compensation algorithm is first designed. The frequency offsets during the Channel Time Extension (CTE) reference period and sample period are estimated separately, and the estimate obtained from the reference period is used to resolve ambiguity in the antenna sample period, which enables high-precision frequency compensation. The CSNN is then constructed to estimate the two-dimensional angle (Fig. 3). Within this framework, a Complex-Valued Convolutional Neural Network (CVCNN) (Fig. 4) is introduced to calibrate the covariance matrix of the received signals, which suppresses correlated noise and multipath interference. Based on the theory of mode-space transformation, the calibrated covariance matrix is projected onto a virtual Uniform Linear Array (ULA). The azimuth and elevation angles are jointly estimated by the ESPRIT algorithm. The estimated angles from three Access Points (APs) are subsequently fused to obtain the final position estimate. Results and Discussions Experiments are conducted to evaluate the performance of the proposed method. For frequency offset suppression, the dual-estimation frequency compensation algorithm markedly reduces the effect on angle estimation, improving estimation accuracy by 91.7% compared with uncorrected data and showing clear improvement over commonly used approaches (Fig. 6). For angle estimation, the CSNN achieves reductions of more than 40% in azimuth error and 25% in elevation error compared with the MUSIC algorithm under simulation conditions (Fig. 7), and verifies the capability of the CVCNN module to suppress various interferences. In practical experiments, the CSNN achieves an average azimuth error of 1.07° and an average elevation error of 1.28° in the training scenario (Table 1, Fig. 10). Generalization experiments conducted in three indoor environments (warehouse, corridor, and office) show that the average angular errors remain low at 2.78° for azimuth and 3.39° for elevation (Table 2, Fig. 11). The proposed method further maintains average positioning accuracies of 28.9 cm in 2D and 36.5 cm in 3D after cross-scene migration (Table 4, Fig. 13). Conclusions The proposed high-precision indoor localization method integrates dual-estimation frequency compensation, the CSNN angle estimation algorithm, and three-AP cooperative localization. It demonstrates strong performance in both simulation and real-environment experiments. The method also maintains stable cross-scene adaptability and accuracy that meet the requirements of high-precision indoor localization.
- Indoor localization,
- Uniform Circular Array (UCA),
- Frequency compensation,
- Complex Subspace Neural Network(CSNN)

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References(16)

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