Three-Dimensional Submarine-to-Submarine Passive Target Tracking in the Presence of Non-Gaussian Noises

Abstract

In underwater surveillance, three-dimensional target tracking is a challenging task. The angles-only measurements (i.e., bearing and elevation) obtained by hull mounted sensors are considered to appraise the target motion parameter. Due to noise in measurements and nonlinearity of the system, it is very hard to find out the target location. For many applications, UKF is best estimator that remaining algorithms. Recently, cubature Kalman filter (CKF) is also popular. It is proposed to use UKF (unscented Kalman filter) and CKF (cubature Kalman filter) algorithms that minimize the noise in measurements. So far, researchers carried out this work (target tracking) in Gaussian noise environment, whereas in this paper same work is carried out for non-Gaussian noise environment. The performance evaluation of the filters using Monte-Carlo simulation and Cramer-Rao lower bound (CRLB) is accomplished and the results are analyzed. Result shows that UKF is well suitable for highly nonlinear systems than CKF.