A Sparse-Reconstruction-Based Fast Localization Algorithm for Mixed Far-Field and Near-Field Sources

FU Shijian; QIU Longhao; LIANG Guolong

doi:10.11999/JEIT250165

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FU Shijian, QIU Longhao, LIANG Guolong. A Sparse-Reconstruction-Based Fast Localization Algorithm for Mixed Far-Field and Near-Field Sources[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250165

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FU Shijian, QIU Longhao, LIANG Guolong. A Sparse-Reconstruction-Based Fast Localization Algorithm for Mixed Far-Field and Near-Field Sources[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250165

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A Sparse-Reconstruction-Based Fast Localization Algorithm for Mixed Far-Field and Near-Field Sources

doi: 10.11999/JEIT250165 cstr: 32379.14.JEIT250165

1.
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

Funds: National Natural Science Foundation of China(62301183)

Received Date: 2025-03-17
Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-13

Abstract

Abstract

Objective Source localization is a key research topic in array signal processing, with applications in radar, sonar, and wireless communications. Conventional localization methods based solely on far-field or near-field models face clear limitations when separating and localizing mixed far-field and near-field sources. Existing approaches, such as subspace-based methods, often show high computational complexity, limited localization accuracy, and degraded performance under low Signal-to-Noise Ratio (SNR) conditions. In addition, many methods assume that near-field sources lie strictly within the Fresnel region, which leads to localization errors and a reduced effective array aperture. Improved algorithms, such as Multiple Sparse Bayesian Learning for Far- and Near-Field Sources (FN-MSBL), overcome part of these limitations and achieve higher localization accuracy. However, their reliance on iterative matrix inversion leads to high computational cost and restricts real-time applicability. Therefore, this study aims to address these issues by proposing a novel algorithm that develops a sparse representation model for mixed far-field and near-field sources in the covariance domain and integrates sparse reconstruction with the Generalized Approximate Message Passing (GAMP) and Variational Bayesian Inference (VBI) frameworks. The objective is to achieve high-precision localization of mixed sources while substantially reducing computational cost. Methods Two algorithms, termed Covariance-Based VBI for Far- and Near-Field Sources (FN-CVBI) and Covariance-Based GAMP-VBI for Far- and Near-Field Sources (FN-GAMP-CVBI), are developed. First, a unified sparse representation model for mixed far-field and near-field sources is constructed based on the covariance vector. This representation benefits from the improved SNR of the covariance vector relative to the original array output, which improves far-field Direction of Arrival (DOA) estimation. Second, to reduce estimation errors in the sample covariance matrix, a pre-whitening operation is applied to the covariance vector to minimize inter-element correlation and improve robustness. Third, a hierarchical Bayesian model is established to impose sparsity, and VBI is employed to estimate model parameters through iterative posterior updates. Fourth, to reduce the computational burden associated with conventional VBI, GAMP is embedded into the VBI framework to replace matrix inversion operations. The detailed implementation of GAMP is given in Table 1. By combining sparse reconstruction, VBI, and GAMP, the proposed approach improves localization accuracy while markedly reducing computational complexity. Results and Discussions The proposed FN-GAMP-CVBI algorithm shows clear improvements in both localization accuracy and computational efficiency. Complexity analysis indicates a substantial reduction in computational cost (Table 2). In terms of localization performance, FN-CVBI and FN-GAMP-CVBI outperform comparative methods, including LOFNS and FN-MSBL (Fig. 3, Fig. 4), particularly under low SNR conditions and with sufficient snapshots (Fig. 5, Fig. 6). The proposed methods also show strong capability in resolving closely spaced far-field sources (Fig. 7). Experimental validation using lake trial data confirms these findings, as reflected by sharper spectral peaks and fewer false peaks in the background noise of the Bearing Time Recording (BTR) results (Fig. 9). FN-CVBI achieves the highest accuracy in far-field DOA estimation and near-field localization. The computational time of FN-GAMP-CVBI is reduced by up to 95% compared with FN-MSBL (Table 4), demonstrating its suitability for real-time applications. Conclusions A sparse-reconstruction-based approach for mixed far-field and near-field source localization is presented by integrating sparse reconstruction with the GAMP-VBI framework. The proposed FN-GAMP-CVBI algorithm addresses the limitations of existing methods and achieves a balanced trade-off between localization accuracy and computational efficiency. Simulation results confirm superior performance, especially under low SNR conditions with sufficient snapshots, and experimental results further support the effectiveness of the approach. The low computational complexity and ability to handle mixed-source scenarios indicate that the proposed algorithm is well suited for real-time localization in complex environments.

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References(19)

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