Sound field reconstruction based on sparse adaptive matching pursuit

The performance of traditional greedy algorithms relies on the accurate knowledge of signal sparsity. To address this limitation, this paper presents a sparsity adaptive matching pursuit algorithm based on compressive sensing. The core advantage of this algorithm is that it eliminates the requirement for prior knowledge of sparsity. By incorporating a phased adaptive mechanism and backtracking principles, the algorithm progressively approximates the true sparsity. It simultaneously refines candidate atom sets to improve the accuracy of support set estimation. Numerical simulations show that the proposed algorithm achieves higher reconstruction accuracy in sound field reconstruction than both orthogonal matching pursuit and equivalent source method, demonstrating its effectiveness and superiority.