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Dimension Reduction Localization Algorithm of Mixed Sources Based on MEMS Vector Hydrophone Array.

Zhenzhen Shang1, Libo Yang1, Wendong Zhang2

  • 1Department of Intelligence and Automation, Taiyuan University, Taiyuan 030032, China.

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|April 23, 2022
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Summary
This summary is machine-generated.

This study introduces a new localization algorithm for Micro-Electro-Mechanical System (MEMS) vector hydrophones. The method efficiently separates near-field and far-field sources with reduced complexity and ambiguity.

Keywords:
MEMS vector hydrophoneMUSIC algorithmdimension reductionmixed sourcesport and starboard ambiguity

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Area of Science:

  • Acoustics
  • Signal Processing
  • Array Signal Processing

Background:

  • Micro-Electro-Mechanical System (MEMS) vector hydrophones are crucial for underwater acoustic detection.
  • Accurate localization of both near-field and far-field sources remains a challenge, especially with linear arrays.
  • Existing algorithms often suffer from high computational complexity and ambiguity issues.

Purpose of the Study:

  • To propose a dimension reduction Multiple Signal Classification (MUSIC) localization algorithm for MEMS vector hydrophone linear arrays.
  • To reduce the localization search space from two-dimensional to one-dimensional.
  • To achieve high-resolution separation of mixed near-field and far-field sources.

Main Methods:

  • Constructing a Lagrangian function via quadratic optimization for azimuth angle estimation.
  • Utilizing the least square method for optimal matching of direction-of-arrivals (DOAs) and ranges.
  • Employing Fresnel zone judgment for near-field source azimuth information and common DOA identification.

Main Results:

  • The proposed algorithm effectively reduces the search dimension, simplifying the localization process.
  • It successfully solves the port and starboard ambiguity problem with a minimal number of array elements.
  • High-resolution separation of far-field and near-field sources is achieved with automatic parameter matching and low computational complexity.

Conclusions:

  • The developed algorithm offers an efficient and accurate solution for localizing multiple sources using MEMS vector hydrophone arrays.
  • It provides a practical approach for engineering applications, overcoming limitations of existing methods.
  • The study demonstrates the potential for improved underwater acoustic detection and source separation.