Prediction of target state using angles-only ensemble Kalman filter

This study deals with the problem of underwater target tracking using bearing and elevation measurements in passive mode without knowing the range measurement. Filtering algorithms are applied to radiated noisy signals to remove the noise which are presented in the acoustic signal and estimate the future position of the target based on the acoustic signal from the target. There is a possibility of the target is nearer to the observer. In such cases, finding out the target and releasing the weapon on to target is very difficult. In this research, for state estimation, the ensemble Kalman filter (EnKF) is proposed. The EnKF is a Monte-Carlo approach to the Bayesian update problem that works well for non-Gaussian and high-dimensional state estimation applications. The Unscented Kalman filter and EnKF are nonlinear filtering techniques for state estimation utilising bearing and elevation measurements are compared in this paper. Noise and model uncertainty are considered during simulations. The simulations were done in MATLAB. The ensemble Kalman filter findings show superior accuracy and measurement resilience than the Unscented Kalman filter in terms of convergence times, indicating that the EnKF is feasible for dynamic state estimation.