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Modified particle filtering algorithm for single acoustic vector sensor DOA tracking.

Xinbo Li1, Haixin Sun2, Liangxu Jiang3

  • 1School of Communication Engineering, Jilin University, Renmin Street No. 5988, Changchun 130022, China. cinple@126.com.

Sensors (Basel, Switzerland)
|October 27, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a modified particle filtering (MPF) algorithm for direction of arrival (DOA) estimation of moving sources using acoustic vector sensors. The MPF-MUSIC algorithm improves accuracy and convergence by considering inter-moment correlations and independently sampling pitch and azimuth angles.

Keywords:
DOA trackingacoustic vector sensorimportance functionparticle filtering

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Area of Science:

  • Acoustics
  • Signal Processing
  • Sensor Technology

Background:

  • Conventional direction of arrival (DOA) estimation algorithms assume static sources, neglecting temporal correlations.
  • Existing particle filtering (PF) methods struggle with large deviations in single-angle estimation and simultaneous two-dimensional angle updates.

Purpose of the Study:

  • To develop an advanced DOA estimation algorithm for moving acoustic sources.
  • To enhance the accuracy and convergence probability of DOA estimation using acoustic vector sensors.

Main Methods:

  • Proposed a modified particle filtering (MPF) algorithm incorporating previous moment's state estimation into particle sampling.
  • Introduced independent sampling and evaluation for pitch and azimuth angles.
  • Integrated the MUSIC spectrum function as the likelihood function, creating the MPF-MUSIC algorithm.

Main Results:

  • The MPF algorithm improves the importance function for better state estimation.
  • The MPF-MUSIC algorithm demonstrates enhanced root mean square error (RMSE) and probability of convergence.
  • Theoretical analysis and simulations validate the proposed algorithms' effectiveness.

Conclusions:

  • The proposed MPF and MPF-MUSIC algorithms effectively address limitations in DOA estimation for moving sources.
  • These methods offer improved performance for acoustic vector sensor applications.