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Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor

Yongsheng Yan1, Haiyan Wang2,3, Xiaohong Shen4,5

  • 1Centre for Infocomm Technology (INFINITUS), School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798 , Singapore. yongsheng@ntu.edu.sg.

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Summary
This summary is machine-generated.

This study introduces efficient acoustic energy-based methods for collaborative source localization, offering cost savings in energy and computation. The proposed techniques effectively solve complex optimization problems for accurate sensor and source positioning.

Keywords:
convex optimizationsemidefinite programmingsensor networkssensor self-localizationsource localization

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Area of Science:

  • Acoustics
  • Signal Processing
  • Optimization Theory

Background:

  • Acoustic energy readings offer an efficient and cost-effective approach for collaborative acoustic source localization.
  • Existing methods face challenges in computational efficiency and handling complex optimization problems.
  • The need for robust localization techniques that account for noise in energy readings is critical.

Purpose of the Study:

  • To develop efficient and computationally feasible methods for acoustic source localization using energy readings.
  • To address the non-convex nature of maximum likelihood optimization problems in this context.
  • To incorporate colored noise into the localization framework and formulate robust optimization problems.

Main Methods:

  • Derivation of maximum likelihood problems based on fused acoustic energy readings from local sensors.
  • Development of an approximate estimator to efficiently solve the non-convex optimization objective.
  • Application of direct norm relaxation and semidefinite relaxation to derive second-order cone programming and semidefinite programming formulations for sensor self-location and source localization.
  • Formulation of minimax optimization problems to account for colored energy reading noise, relaxed into convex optimization problems.

Main Results:

  • The proposed approximate estimator effectively addresses the non-convex optimization problem in acoustic source localization.
  • Relaxation techniques yield convex optimization problems (SOCP, SDP) suitable for practical implementation.
  • The methods demonstrate validity and improved performance compared to existing acoustic energy-based localization techniques.
  • The formulated minimax optimization problems provide robustness against colored noise.

Conclusions:

  • The developed methods offer efficient and computationally attractive solutions for collaborative acoustic source localization.
  • The use of relaxation techniques simplifies complex optimization problems, enhancing practical applicability.
  • The proposed approach is effective even when dealing with colored noise in energy readings.