Estimating a Boat’s Vertical Velocity with Unpositioned 6DOF IMU:s : How sensor fusion and knowledge of the system dynamics can be used to estimate the IMU positions and produce fused estimates

Abstract: Longline fishing is a method of fishing that utilizes baited hooks to catch fish in an environmentally friendly way. In order to reduce the number of catch lost while longline fishing, it is of great interest to be able to keep an even tension on the fishing line. This can be done by estimating the speed at the point of interest (POI) at which the fishing line is attached to the boat. Due to the harsh conditionson the seas, it is not recommended to put any sensors directly at that point. The aim of this thesis was to explore whether or not it is possible to estimate the vertical speed at the POI by having sensors measuring linear acceleration and angular velocity at various unknown places in the boat. The sensors were placed at various places in a simulated boat, after which the sensor orientations and positions were calculated using a nonlinear Least Squares method. After the sensors were positioned, an Extended Kalman Filter (EKF) was implemented on each sensor, after which the speed of the POI was calculated as the fused estimate of all EKFs. By changing the number of sensors and their sampling times, the best compromise between accuracy, computational load and number of sensors was found. The results prove that it is fully possible to estimate the vertical speed of the POI using only four 6DOF IMU:s using a sampling time of 50 or 100 ms, depending on how accurate the user wants the estimated positions of the sensors to be. However, there are still many ways in which the method used can be improved to geta better estimate.