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A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources

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Abstract

In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays.

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Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

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Authors and Affiliations

  1. Key Laboratory of Adaptive Optics, Chinese Academy of Sciences, Chengdu, 610209, Sichuan, China

    Kejun Yin & Yun Dai

  2. School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China

    Kejun Yin & Chunming Gao

  3. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, 610209, Sichuan, China

    Kejun Yin & Yun Dai

Authors
  1. Kejun Yin
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  2. Yun Dai
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  3. Chunming Gao
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Correspondence to Kejun Yin.

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Yin, K., Dai, Y. & Gao, C. A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources. Circuits Syst Signal Process 41, 6547–6559 (2022). https://doi.org/10.1007/s00034-022-02065-9

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  • DOI: https://doi.org/10.1007/s00034-022-02065-9

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