Underwater surveillance in non-Gaussian noisy environment

Abstract

The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly throughout the process and (b) repeatedly at some particular time samples. The efficiency of these filters with the increase in non-Gaussian noise samples is discussed in this paper. The performance of filters is compared with that of Cramer-Rao Lower Bound. Doppler-bearing extended Kalman filter and Doppler-bearing unscented Kalman filter are chosen for this work.

Measure of nonlinearity for underwater target tracking using hull-mounted sensor

“…From Eqn. (10) in (Omkar Lakshmi Jagan, 2020), the measurement equation of dimension Z is given as follows:…”

“…The discrete-time dynamic systems for both problems are described in Eqns. (8) and (11) in Omkar Lakshmi Jagan and Koteswara Rao (2020) MoNL is analyzed by considering the cycle of the filtering algorithms in which the updation and prediction steps are considered during the time interval. To analyzed MoNL, Eqns.…”

“…To analyzed MoNL, Eqns. (8) and (11) in Omkar Lakshmi Jagan and Koteswara Rao (2020) are combined to form a single equation as follows:…”

“…Mathematical modeling of DBPF-EKF and MoNL is given in Section 2. Mathematical modeling of DBEKF and DBUKF algorithms is explained in (Omkar Lakshmi Jagan, 2020). Simulation results are explained briefly in Section 3.…”

Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter

“…Bearings-only tracking (BOT) comprises estimating a target's state from bearing measurements received by an observer [1]. It plays an important role in both military and civil applications, for example, underwater surveillance [2,3], 3-D passive target tracking [4][5][6][7] and UAV (unmanned aerial vehicle) path planning [8][9][10][11]. However, the nonlinear relationship between the bearing measurements and the target state vector makes BOT a typical nonlinear filtering problem [12].…”