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INS/DVL positioning system using Kalman filter”. Australian journal of basic and applied sciences
"... Abstract: We proposed the design and programmingimplementation of data fusion algorithm of INS system and external DVL sensor to optimize the variables estimation such as position, velocity, and direction in three orientations in local navigation system. Benefit from Kalman filter and data fusion ..."
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Abstract: We proposed the design and programmingimplementation of data fusion algorithm of INS system and external DVL sensor to optimize the variables estimation such as position, velocity, and direction in three orientations in local navigation system. Benefit from Kalman filter and data fusion of INS system and DVL sensor, the system errors including bias errors, scale factor error of accelerometer and gyros have almost been eliminated and the corrected and real path are close to each other. The data of submarine in 125 minutes is used as an INS. The sample time is considered 0.5 s. Therefore, the number of samples will be 15000. Measuring the submarine velocity is carried out by DVL at the presence of zeromean white noise. The noise value is such that the average of DVL velocity error to be 2%. After passing 7500 s, it can be deduced that the difference between primary path and corrected path is one meter.