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

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

doi:10.11999/JEIT250395

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JIANG Wei, ZHI Boxin, YANG Junjie, WAN 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, WAN 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)

Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-14

Abstract

Abstract

Objective With the growing demand for high-precision indoor localization in intelligent service scenarios, existing positioning technologies still face significant challenges in complex environments, where factors such as signal frequency offset, multipath propagation, and noise interference severely degrade localization accuracy. To address these challenges, this paper proposes a 3D localization method with a uniform circular array (UCA) driven by Complex Subspace Neural Network (CSNN), aiming to enhance accuracy and robustness in complex environments. Methods The proposed method establishes a complete localization pipeline, based on the hierarchical signal processing framework, encompassing frequency offset compensation, two-dimensional angle estimation, and spatial mapping (Fig. 2). Firstly, a dual-estimation frequency compensation algorithm is proposed. By separately estimating the frequency offsets during the CTE reference period and sample period, the frequency estimate obtained from the reference period of the CTE signal is used to disambiguate the frequency estimation in the antenna sample period, enabling high-precision frequency compensation. Subsequently, the CSNN algorithm is constructed to estimate two-dimensional angle (Fig. 3) in which Complex-Valued Convolutional Neural Network (CVCNN) (Fig. 4) is introduced to calibrate the covariance matrix of received signals, effectively suppressing correlated noise and multipath interference. Furthermore, based on the theory of mode space transformation, the calibrated covariance matrix is projected onto a virtual uniform linear array. Then the azimuth and elevation angles are jointly estimated by the ESPRIT algorithm. Finally, the estimated angles from three access points (AP) are fused to achieve position estimation. Results and Discussions The experiments are conducted to evaluate the performance of the proposed method. For frequency offset suppression, the proposed dual-estimation frequency compensation algorithm significantly reduces the adverse impact on angle estimation, improving estimation accuracy by 91.7% compared to uncorrected data and showing clear improvements over commonly used methods (Fig. 6). Regarding angle estimation, the CSNN algorithm achieves over 40% and 25% error reduction in azimuth and elevation, respectively, compared to the MUSIC algorithm under simulation conditions (Fig. 7), and also verifies the CVCNN module’s capability to suppress various interferences. In practical experiments, the CSNN algorithm achieves an average azimuth error of 1.07° and an elevation error of 1.28° in the training scenario (Table 1, Fig. 10). Moreover, generalization experiments in three distinct 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). Finally, the proposed method maintains an average positioning accuracy 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, CSNN angle estimation algorithm and three-AP cooperative localization. It has excellent performance in both simulation and real-environment experiments. The method exhibits strong cross-scene adaptability accuracy which meets the requirements of high-precision indoor localization.
- Indoor Localization,
- Uniform Circular Array,
- Frequency Compensation,
- Complex Subspace Neural Network

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References(16)

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