Target tracking using bearings-only measurements in passive mode operation of sonar is a crucial issue of underwater tracking. Target motion in underwater scenario is analyzed using bearings-only measurements and calculating parameters like range, course and speed of the target. This is called Target Motion Analysis (TMA). TMA process is highly non-linear as the measurements chosen are nonlinearly related to the selected target state vector and the traditional, optimal linear Kalman filter will not be appropriate to use. It is presumed that the target is moving in straight line path with constant velocity, so Extended Kalman Filter (EKF) is proposed in this paper. The algorithm is simulated for several scenarios using MATLAB. Monte-Carlo runs are performed to evaluate the capability of the algorithm.
Inactive target tracking using bearings-only measurements is a crucial issue of underwater tracking.Target Motion Analysis (TMA) process is highly non-linear so the non linear algorithms like Modified Gain Bearings-only Extended Kalman Filter (MGBEKF) and Unscented Kalman Filter (UKF) are implemented and their performance is evaluated based on their solution convergence times. It is presumed that the target is moving in straight line path with constant speed. The algorithms are simulated for several scenarios which are close to reality using MATLAB. Monte-Carlo runs are performed to evaluate the capability of the algorithms.
Using the recently proposed measure of nonlinearity (MoN), the authors try to find the magnitude of nonlinearity for passive target tracking with bearings-only measurements in underwater environment. The method derived to measure the nonlinearity is completely based on the state covariance matrices of the filters. It is tried to find the allowable magnitude of nonlinearity in terms of MoN with which a filter can perform to estimate the target motion parameters with required accuracy. In this paper, MoN values for different filters are computed for different scenarios. Results obtained in the Monte Carlo simulation are presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.