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Doppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment.

The system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation.

In this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem.

Algorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.

This paper aims to target tracking in the marine environment is typically obtained by considering the measurement parameters like frequency, elevation and bearing. Marine environmental surveillance provides critical information and assistance for the exploitation and maintenance of marine resources.

With the use of intelligent sensor techniques like Hull-mounted and towed array sensors, convenient, precise and dependable three-dimensional (3D) underwater target tracking is introduced.

This research investigates a method to develop a reliable Unscented Kalman Filter (UKF) algorithm for enhanced underwater target tracking in a 3D scenario by using bearing, frequency and elevation measurements. In applications for underwater target tracking, uncertainty and inaccuracies are typically described by using Gaussian additive noise.

The proposed UKF algorithm is tested and analyzed using 100 Monte Carlo simulations with the Gaussian generated noise.