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

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