Open AccessTracking of an underwater source using sparse method
Author(s) -
Resi Pratiwi,
Dhany Arifianto,
Elok Anggrayni,
Endang Widjiati,
O Sukirman,
R. Amelia
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1896/1/012024
Subject(s) - compressed sensing , matching pursuit , underwater , signal reconstruction , minification , signal (programming language) , computer science , algorithm , artificial intelligence , process (computing) , computer vision , pattern recognition (psychology) , signal processing , telecommunications , radar , oceanography , operating system , programming language , geology
The sparse method or better known as compressed sensing (CS), is a method often used for the signal reconstruction process. This method had considered better than conventional methods because it can reconstruct a signal with a smaller amount of data. Many algorithms had used for signal reconstruction using the CS method, including l 1 -minimization and orthogonal matching pursuit (OMP). In this study, the two algorithms were used for signal reconstruction of underwater objects and then compared to find out which algorithm is better for the signal reconstruction of underwater objects. Comparing the two algorithms had based on parameters in the form of PSNR and RMSE against sparsity. Based on the simulations that had been doing, known that the l 1 -minimization algorithm can reconstruct signal up to 40% sparsity. Whereas the OMP algorithm can only reconstruct signals up to 30% sparsity. PSNR and RMSE generated from the l 1 -minimization algorithm show that this algorithm provides better reconstruction results than OMP for underwater object signals. The results obtained show that the best tracking process is at an angle of incidence of 90°.