Target tracking is one the most fundamental elements of a radar system. The aim of
target tracking is the reliable estimation of a target' / s true state based on a time
history of noisy sensor observations. In real life, the sensor data may include
substantial noise. This noise can render the raw sensor data unsuitable to be used
directly. Instead, we must filter the noise, preferably in an optimal manner. For
land, air and surface marine vehicles, very successful filtering methods are
developed. However, because of the significant differences in the underwater
propagation environment and the associated differences in the corresponding
sensors, the successful use of similar principles and techniques in an underwater
scenario is still an active topic of research. A comparative study of the effects of the
underwater environment on a number of tracking algorithms is the focus of the
present thesis. The tracking algorithms inspected are: the Kalman Filter, the
Extended Kalman Filter and the Particle Filter. We also investigate in particular the
IMM extension to KF and EKF filters. These algorithms are tested under several
underwater environment scenarios.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/2/12608866/index.pdf |
| Date | 01 October 2007 |
| Creators | Ege, Emre |
| Contributors | Saranli, Afsar |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for METU campus |
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