Fourth-Order Cumulant-Based Deconvolution Algorithm With Vector-Sensor Arrays for Acoustic Imaging

This paper presents a deconvolution algorithm for high resolution acoustic imaging, specifically tailored for small-element acoustic vector-sensor (AVS) arrays. While existing fourth-order cumulant (FOC) based methods can improve spatial resolution, their localization accuracy remains fundamentally limited by the inadequate spatial sampling of small arrays. To address this limitation, the deconvolution method is developed based on a forward propagation model for sound sources received by AVS arrays. The proposed method is designed to achieve high-precision localization under challenging conditions, specifically at low signal-to-noise ratios (SNR) and in the presence of spatial aliasing. The FOC-based vector beamforming is initially performed to obtain the “dirty map”, with the FOC matrix iteratively updated to progressively remove sidelobe components merged in the main lobe. By incorporating coherence information, the proposed cumulant-based vector CLEAN-SC (CUM-VCLEAN-SC) method effectively mitigates spatial resolution degradation caused by the sidelobes of coherent sources in small-element arrays. Meanwhile, the clean source intensity power is estimated based on the main lobe peak, and iterations continue until convergence, ultimately yielding the high-resolution acoustic imaging results. Simulation results demonstrate that the proposed method significantly enhances imaging resolution and localization accuracy compared to traditional algorithms, while maintaining robustness in low SNR environments and in the presence of spatial aliasing caused by high frequencies. Furthermore, under minimal planar array configurations, the method exhibits superior robustness over traditional acoustic imaging algorithms. Experimental results in a semi-anechoic chamber further confirm the effectiveness of the proposed algorithm with small-element arrays.