Adaptation of the hybrid deconvolution approach for localization of static, rotating and linearly moving sources using the LASSO method

In industry, sound source localization technology is used for detecting and diagnosing equipment faults by precisely locating noise sources to detect normal or abnormal functioning, and to improve the maintenance efficiency. However, the localization of multiple sound sources with various motion patterns is often hindered by the different motion characteristics and spatial distributions of these sources, making their distinction difficult for traditional methods. This complex problem of localizing sound sources with different motion patterns is addressed in this study, with a specific focus on the identification and separation of static, linearly moving, and rotating sound sources. An innovative method is proposed that integrates Modal Composition Beamforming (MCB) with the equivalent source approach. A Multi-Motion Mode Sound Source Power Propagation(M3-S2-PP) model is introduced and hybrid deconvolution approach is considered in relation to sources of the static, linearly moving and rotating types. Cross-PSF matrix equation is effectively solved using Least Absolute Shrinkage and Selection Operator (LASSO) within Alternating Direction Method of Multipliers (ADMM). LASSO's regularization capability enhances predictive accuracy and promotes solution sparsity, enabling the effective identification of active sound sources. In a first step, extensive simulations are conducted to rigorously test the effectiveness of this method and to explore how the simulation scenario parameters influence the localization results. In scenarios where the MCB method is applicable, the proposed method achieves precise separation and localization results. In scenarios where the application of the MCB method may produce side lobes, the localization results are observed to exhibit varying degrees of deviation as the experimental scenario parameters change. Then experiments are carried out to rigorously examine the effectiveness of this technique and confirm the applicability conditions. Accuracy, reliability, and robustness of this method are consistently validated across various scenarios. Even under challenging conditions, accurate performance in noise immunity and sound source localization accuracy is demonstrated by this method. Potential applications in fields such as industrial monitoring, environmental noise assessment, and acoustic imaging are indicated by its performance.