3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference

3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference

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The propeller tip vortex cavitation (TVC) localization problem involves the separation Ash Catchers of noise sources in proximity.This work describes a sparse localization method for off-grid cavitations to estimates their precise locations while keeping reasonable computational efficiency.It adopts two different grid (pairwise off-grid) sets with a moderate grid interval and provides redundant representations for adjacent noise sources.To estimate the position of the off-grid cavitations, a block-sparse Bayesian learning-based method is adopted for the pairwise off-grid scheme (pairwise off-grid BSBL), which iteratively updates the grid points using Bayesian inference.Subsequently, simulation and experimental results demonstrate that the proposed method achieves the separation of adjacent off-grid cavitations with reduced computational cost, while the other Wooden Dollhouse Accessories scheme suffers from a heavy computational burden; for the separation of adjacent off-grid cavitations, the pairwise off-grid BSBL took significantly less time (29 s) compared with the time taken by the conventional off-grid BSBL (2923 s).