Navigation of Autonomous Underwater Vehicle Using Extended Kalman Filter
To navigate the Autonomous Underwater Vehicle (AUV) accurately is one of the most important aspects in its application. A truly autonomous vehicle must determine its position which requires the optimal integration of all available attitude and velocity si
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Abstract. To navigate the Autonomous Underwater Vehicle (AUV) accurately is one of the most important aspects in its application. A truly autonomous vehicle must determine its position which requires the optimal integration of all available attitude and velocity signals. This paper investigates the extended Kalman Filtering (EKF) method to merge asynchronous heading, attitude, velocity and Global Positioning System (GPS) information to produce a single state vector. Dead reckoning determines the vehicle’s position by calculating the distance travelled using its measured speed and time interval. The vehicle takes GPS fixes whenever available to reduce the position error and fuses the measurements for position estimation. The implementation of this algorithm with EKF provides better tracking of the trajectory for underwater missions of longer durations. Keywords: Navigation, Extended Kalman Filter (EKF), Autonomous Underwater Vehicle (AUV), data fusion.
1 Introduction Precise navigation remains a substantial challenge to all platforms moving underwater [1] due to unavailability of GPS signals. With the emergence of new applications and the growing acceptance of AUVs for both military and civilian use, the need for enhanced accuracy and robustness in under-water navigation is required. Also this leads to the emerging applications like fully autonomous naval operations for mine detection, polar deployments and under ice surveys for oceanographic research, pipeline inspection and other works related to oil exploration [2],[3]. The process and sensor noise levels present are responsible for errors in the position estimated which is important for navigating underwater autonomously. Surface navigation of AUV can be accomplished with the use of the GPS. However, the high frequency signals from GPS satellites cannot travel through the water to the AUV. Therefore, another method needs to be incorporated in to the system for under-water navigation. Methods like geophysical, acoustic and image-based navigation can also be used for AUV trajectory tracking [4]. An accurate map of the environment combined with measurements of geophysical parameters, such as bathymetry, magnetic field, or P. Vadakkepat et al. (Eds.): FIRA 2010, CCIS 103, pp. 1–9, 2010. © Springer-Verlag Berlin Heidelberg 2010
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T.N Ranjan, A. Nherakkol, and G. Navelkar
gravitational anomalies is required for geophysical and image-based navigation and is technically not viable for a survey application. Acoustic navigation requires deployment of array of acoustic transducers to track the vehicle trajectory. Dead reckoning is a more graceful solution in such applications.
2 Dead Reckoning Dead reckoning is the process of measuring vehicle attitude (roll, pitch, and yaw) and velocity (forward, lateral, and water body currents) and integrating these measurements over time from an initial position to determine the current position. Attitude and velocity measurement errors will cause the integrated position estimate to deviate from the true position over time. Depen
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