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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 l1-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 l1-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 l1-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°.

This experiment conduct in mini semi-anechoic water test tank and towing tank at IHL. The purpose is to analyse performance from ALS algorithm in dimension, salinity and temperature water environment. For this purpose we varied water salinity and temperature in mini semi-anechoic water test tank, and for different dimension we took the experiment on two different size of test tank. The results showed that ALS have consistent result at salinity and temperature variety but at large dimension test tank 200 × 10 × 5.5 m without variation on the water medium indicates an anomaly in the results of the ALS BSS technique performance.