改进的稀疏度自适应多路径匹配追踪算法

Using the signal reconstruction algorithm, an improved multipath matching pursuit (MMP) algorithm with sparsity self-adaption is proposed to solve the problems of large calculated amounts and unknown signal sparsity in the tree-based MMP depth-first algorithm. This algorithm introduces adaptive thinking into the MMP algorithm, the number of support set atoms is adaptively selected, and the path with the largest atomic matching probability is selected through pruning technology. This makes for a more effective algorithmic search path and greatly reduces the amount of calculation. The algorithm improves the inner product matching criterion, which can more accurately and efficiently select the atoms that match the residual signal in the measurement matrix. In the iterative process, support set atom backtracking and the variable step size method are used to improve the reconstruction accuracy. Compared with the traditional MMP algorithm, the proposed algorithm has a shorter calculation time, greater reconstruction accuracy, and greater practical application value.