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Related Experiment Video

Updated: May 24, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Fast implementation of sparse iterative covariance-based estimation for source localization.

Qilin Zhang1, Habti Abeida, Ming Xue

  • 1Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida 32611, USA.

The Journal of the Acoustical Society of America
|February 23, 2012
PubMed
Summary
This summary is machine-generated.

Fast implementations of the sparse iterative covariance-based estimation (SPICE) algorithm improve source localization using uniform linear arrays. Exploiting matrix structures reduces computational complexity for enhanced array processing performance.

Related Experiment Videos

Last Updated: May 24, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Area of Science:

  • Signal Processing
  • Array Processing
  • Acoustics

Background:

  • Sparse Iterative Covariance-based Estimation (SPICE) offers high-resolution source localization but suffers from high computational complexity.
  • Traditional SPICE is parameter-free and robust, outperforming delay-and-sum beamforming in resolution and sidelobe levels.
  • Computational demands increase significantly with higher dimensional data in SPICE.

Purpose of the Study:

  • To develop fast implementations of the SPICE algorithm for source localization with uniform linear arrays (ULAs).
  • To extend SPICE to acoustic vector-sensor ULAs under specific noise conditions.
  • To demonstrate significant computational gains through proposed algorithmic enhancements.

Main Methods:

  • Exploited the Toeplitz structure of the array output covariance matrix using Gohberg-Semencul factorization to mitigate SPICE complexity.
  • Extended SPICE to acoustic vector-sensor ULAs with a nonuniform white noise assumption.
  • Developed fast implementations leveraging block Toeplitz properties for the extended SPICE algorithm.

Main Results:

  • Achieved significant reductions in computational complexity for SPICE algorithm implementations.
  • Successfully extended SPICE to acoustic vector-sensor arrays, maintaining performance benefits.
  • Numerical simulations confirmed substantial computational gains for the proposed fast SPICE methods.

Conclusions:

  • Fast SPICE implementations effectively reduce computational load for source localization with ULAs.
  • The extended SPICE algorithm provides efficient solutions for acoustic vector-sensor array processing.
  • Proposed methods offer practical advantages for high-resolution array processing applications.