Open AccessA Novel Sparse Data Reconstruction Algorithm for Dynamically Detect and Adjust Signal Sparsity
Author(s) -
Dongxue Lu,
Zengke Wang
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.61
Subject(s) - algorithm , matching pursuit , computer science , greedy algorithm , signal (programming language) , residual , spare part , matching (statistics) , process (computing) , function (biology) , energy (signal processing) , compressed sensing , mathematical optimization , mathematics , statistics , marketing , evolutionary biology , business , biology , programming language , operating system
This paper proposed a novel algorithm which is called the joint step-size matching pursuit algorithm (JsTMP) to solve the issue of calculating the unknown signal sparsity. The proposed algorithm falls into the general category of greedy algorithms. In the process of iteration, this method can adjust the step size and correct the indices of the estimated support that were erroneously selected in a dynamical way. And it uses the dynamical step sizes to increase the estimated sparsity level when the energy of the residual is less than half of that of the measurement vectory. The main innovations include two aspects: 1) The high probability of exact reconstruction, comparable to other classical greedy algorithms reconstruct arbitrary spare signal. 2) The sinh() function is used to adjust the right step with the value of the objective function in the late iteration. Finally, by following this approach, the simulation results show that the proposed algorithm outperforms state of- the-art similar algorithms used for solving the same problem.