Trajectory and Pulse Optimization for Active Towed Array Sonar using MPC and Information Measures

• Main Author: Ekdahl Filipsson, Fabian
• Format: Others
• Language: English
• Published: Uppsala universitet, Avdelningen för systemteknik 2020
• Subjects: Active Towed Array Sonar; ATAS; Extended Kalman filter; EKF; Unscented Kalman filter; UKF; Information measures; Model predictive control; MPC; Signal processing; Sensor management; Pulse forms; Route planning; Trace; Determinant; Frobenius norm; Signal Processing; Signalbehandling
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420532

In underwater tracking and surveillance, the active towed array sonar presents a way of discovering and tracking adversarial submerged targets that try to stay hidden. The configuration consist of listening and emitting hydrophones towed behind a ship. Moreover, it has inherent limitations, and the characteristics of sound in the ocean are complex. By varying the pulse form emitted and the trajectory of the ship the measurement accuracy may be improved. This type of optimization constitutes a sensor management problem. In this thesis, a model of the tracking scenario has been constructed derived from Cramér-Rao bound analyses. A model predictive control approach together with information measures have been used to optimize a filter's estimated state of the target. For the simulations, the MATLAB environment has been used. Different combinations of decision horizons, information measures and variations of the Kalman filter have been studied. It has been found that the accuracy of the Extended Kalman filter is too low to give consistent results given the studied information measures. However, the Unscented Kalman filter is sufficient for this purpose.