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

Bo Guo,Cui Wang,Jianye Ma,Jerzy Baranowski

doi:10.1155/2021/3625362

Bo Guo, Cui Wang + Show 2 more

Open Access

https://doi.org/10.1155/2021/3625362

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Abstract

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.

Highlights

Unmanned or autonomous underwater vehicles are increasingly applied to underwater science data acquisition, such as underwater monitoring, exploration, disaster prevention, and rescue
E standard deviation of predicted noise ranged from 0.01 m to 0.11 m, and the standard deviation of observed noise ranged from 0.02 m to 0.2 m. e ideal attenuation coefficients under different parameters were calculated through simulations
The initial acceleration is set to 200 m/s2, and the Time of Arrival (TOA) and Time Difference of Arrival (TDOA) fusion positioning algorithm based on Extended Kalman filter (EKF) is still taken as an example, and 100 s is calculated continuously. e residual error and the maximum divergence value of the positioning process are recorded, respectively

Summary

Introduction

Unmanned or autonomous underwater vehicles are increasingly applied to underwater science data acquisition, such as underwater monitoring, exploration, disaster prevention, and rescue. Underwater acoustic methods play a significant role in the course of the positioning of underwater vehicles [1, 2]. In many underwater acoustic positioning methods, there are inevitably various random errors [3], such as system disturbance, observation error [4], and non-line-ofsight error [5]. Random errors conform to certain statistical laws in multiple positioning results, it is impossible to predict the size and direction of a single error. In order to reduce errors, digital filtering is an essential part for positioning algorithm. The Kalman filter makes noise suppression more effective, and positioning accuracy further improved because it does not impose too strict limits on error forms and its unique data fusion function

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