Sound field reconstruction using compressed modal equivalent point source method.

Area of Science:

• Acoustics
• Signal Processing
• Computational Physics

Background:

• Near-field acoustic holography (NAH) accuracy, resolution, and cost depend heavily on spatial sampling points.
• Increased sampling points enhance accuracy but raise measurement workload and hardware expenses.
• Compressive sensing (CS) offers a solution by leveraging signal sparsity to reduce sampling requirements.

Purpose of the Study:

• To propose a novel method, the compressed modal equivalent point source method (CMESM), based on compressive sensing (CS) theory.
• To reduce the number of spatial sampling points required in NAH while maintaining high-resolution reconstruction.
• To improve the efficiency and cost-effectiveness of NAH measurements.

Main Methods:

• Developed the compressed modal equivalent point source method (CMESM).
• Utilized a sparse basis derived from eigen-decomposition of the power resistance matrix to compress equivalent point source strengths.
• Applied ℓ1-norm minimization to enforce sparse solutions.

Main Results:

• Demonstrated the validity of CMESM through numerical simulations and experimental validation.
• Showcased CMESM's superior performance compared to existing methods when reducing spatial sampling points.
• Achieved high-resolution acoustic reconstruction with fewer measurements.

Conclusions:

• The proposed CMESM effectively reduces spatial sampling points in NAH.
• CMESM provides a valid and advantageous approach for high-resolution acoustic imaging with reduced data acquisition.
• This method offers a practical solution for lowering the cost and workload associated with NAH.