Implementation Of underwater target tracking techniques for Gaussian and non-Gaussian environments

Abstract

There are several techniques implemented, in an underwater target tracking environment, for the nonlinear dynamic systems in Gaussian and non-Gaussian environments. It is assumed with non-Gaussian distribution to make the problem part of the non-Gaussian distribution, and is measured in terms of calculations of plenty of scenarios simulated to validate the potential of the sub-optimal filter.This research is further carried out by considering two categories of non-Gaussian noises i.e.a mixture of Gaussian noises and shot noise. To evaluate tracking in Gaussian and non-Gaussian noises, the suboptimal filters, Extended Kalman filter, and Unscented Kalman filter (UKF algorithms are considered. Gaussian noise is a statistical noise having probability density function equal to the normal distribution function. The suboptimal filters, Extended Kalman filter, and Unscented Kalman filter (UKF) algorithms are considered to evaluate tracking in Gaussian and non-Gaussian noises. To make further evaluation of the above said algorithms, they are compared with theoretical Cramer-Rao lower bound. The efficiency of UKF is in terms of percentage of non-Gaussian noise corrupted measurements, for which solution is obtained within a short time. The application of Monte-Carlo method at this simulations trapped accurate results.