Least Squares Support Vector Machine Beamforming Algorithm

Least Squares Support Vector Machine Beamforming Algorithm

Authors

  • Shiyu Lu Hangzhou Applied Acoustics Research Institute, Hangzhou 310023, China
  • Xiangbo Sun Hangzhou Applied Acoustics Research Institute, Hangzhou 310023, China
  • Feng Ding Hangzhou Applied Acoustics Research Institute, Hangzhou 310023, China

DOI:

https://doi.org/10.53469/wjimt.2025.08(01).12

Keywords:

Beamforming, Least Squares Method, Support Vector Machine, Far-field Plane Wave

Abstract

The article primarily proposes a beamforming method based on the Least Squares Support Vector Machine (LSSVM). Using a uniform horizontal line array as an example, it explores the design of plane wave beamforming under the far-field assumption. Unlike conventional methods of weighted steering vector and copied correlation beamforming for uniform horizontal line arrays, the article suggests using the Least Squares Support Vector Machine algorithm to establish a learning relationship between received and desired signals, replacing steering vectors with learned weights, and substituting copied correlation operations with kernel operations for beamforming. Simulation verification shows that compared to conventional beamforming methods, this approach achieves narrower mainlobe widths and lower sidelobe levels, providing theoretical support for further engineering applications.

References

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Published

2025-01-19

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