In this paper, a new application of the Compressed Sensing (CS) theory to the data transmission problem in oceanographic large-scale monitoring missions is proposed. The amount of the data (temperature, salinity and so on) collected during this mission can be huge and the transmission process could become prohibitively expensive in terms of both battery consumption and monetary cost of the satellite link. A new CS-based algorithm is thus developed in order to attain a cost-preserving and energy efficient data transmission. Moreover, the performance of the proposed CS algorithm is investigated with various parameter settings (different sensing and representation matrices, classical L1 and fast L0 minimization algorithms) using real oceanographic data.
Oceanographic Data Transmission: a Compressed Sensing-based Approach
GINI, FULVIO;GRECO, MARIA
2013-01-01
Abstract
In this paper, a new application of the Compressed Sensing (CS) theory to the data transmission problem in oceanographic large-scale monitoring missions is proposed. The amount of the data (temperature, salinity and so on) collected during this mission can be huge and the transmission process could become prohibitively expensive in terms of both battery consumption and monetary cost of the satellite link. A new CS-based algorithm is thus developed in order to attain a cost-preserving and energy efficient data transmission. Moreover, the performance of the proposed CS algorithm is investigated with various parameter settings (different sensing and representation matrices, classical L1 and fast L0 minimization algorithms) using real oceanographic data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
