Signal Identification of Underwater Objects Using Autoregressive Moving Average with Exogenous excitation Model

This electronic document This paper aims the signal identification of underwater objects using Autoregressive Moving Average with Exogenous excitation model in such a way that the outcome of the model is like actual measurements. It is used for parameter estimation. This will be validated by comparing the output of the actual system with the output generated by ARMX and various other models including autoregressive with exogenous variables and Box-Jenkins models, for the same given input signal. Mean square error criterion will be utilized to evaluate the results in frequency and time domains. Initial results illustrate that ARMX predicts the acoustic scattering response with an accuracy of 97% while ARX provides an accuracy of 78% and BJ model poorly estimates the signal with an accuracy of 35%. ARMX also provides a higher accuracy of detection by 7-8% as compared to existing methodologies hence proving to be a better option than current strategies