A Dynamic-Weighted Attenuation Memory Extended Kalman Filter Algorithm and Its Application in the Underwater Positioning

Extended Kalman filter (EKF) plays an important role in the acoustic signal processing of underwater positioning. However, accumulative errors and model inaccuracies lead to divergence. Then, attenuation memory EKF is created response this issue which needs manually select all or part parameters. Thus, a dynamic-weighted proposed. Firstly, several positioning simulations under different conditions are carried out. Results show, with change parameter positioning, ideal coefficient changes between 0.5 1, but it difficult express function formula statistical form. Secondly, dynamic selection method factor designed. In later contrast simulation, proposed has improved performance compared existing algorithm. Finally, results physical verification experiment show that algorithm not only suppresses divergence better also avoids subjectivity certain extent.