Data compression for underwater glider system using frequency sampling filters

Abstract

Data acquisition from underwater glider system yields large amounts of data. This may lead to a long computational time during the identification process. The raw data may also contain complex system disturbance information which may require a sophisticated optimization algorithm to achieve desirable results. In this paper, frequency sampling filters approach is used as a tool to compress the data and obtain meaningful parameter that describes the empirical model of the system. The use of finite impulse response model and the maximum likelihood method will play a role in eliminating the bias and noise effects of the glider data systems. By performing this procedure, the compressed, cleaned and unbiased data will be obtained, in which, it can be further used to develop a model of the glider system, also to analyze and observe for optimization and controller design purposes.