Terrain aided navigation

In many autonomous underwater vehicle (AUV) applications, a basic problem is to determine the AUV's position accurately. Terrain aided navigation, which supports the existing inertial navigation system with terrain information, is a promising method. In this thesis, the Kalman filter and the Bayesian approach implemented as point mass filter are used in terrain aided navigation. To formulate a linear Kalman filter, the measured depths are compared to a reference map to determine the position. During this matching procedure, both maximum likelihood estimation and maximum a posteriori estimation are implemented and analyzed. The performances of the Kalman filter and point mass filter are compared with each other, and also with the Cramer-Rao lower bound. ii iii Acknowledgement This master thesis is carried out with the Signal Processing Group in the Department of Signals, Sensors and Systems at the Royal Institute of Technology (KTH). I would like to sincerely thank my advisor Magnus Jansson for his continuous and extensive support throughout this thesis work. Without his helpful suggestions and guidance, this work can never be done. I also wish to express my gratitude to the members in the Signal Processing Group for their kindness and their valuable advice. My classmates in the same master program in Wireless Systems deserve my special thanks for their help and encouragement from day one. Finally, I would like to thank my family members for their endless love and support during all my past years of study.