A Novel Adaptive Filter for Cooperative Localization Under Time-Varying Delay and Non-Gaussian Noise

In this study, to cope with time-varying delay in acoustic communication under non-Gaussian noise caused by outliers, a novel robust delay filtering algorithm is proposed for cooperative localization of autonomous underwater vehicles (AUVs). First, the modified measurement equations of the nonlinear cooperative location system with time-varying delay are derived, and the delay caused by the information processing and propagation of the underwater acoustic modem is converted into measurement bias. Second, to improve the robustness of the system with outliers caused by abnormal measurement values of underwater acoustic communication and Doppler velocity log (DVL), the statistical similarity measure (SSM) is introduced to construct the cost function. This is accomplished by quantifying the similarity between the state vector and the predicted state vector, similarity between the delay measurement, and modified predicted delay measurement. Finally, the modified measurement noise variance is obtained by maximizing the cost function, and two algorithms for the approximate solution of the nonlinear cost function are presented. The proposed robust delayed algorithm alleviates the impacts of delayed measurement with outliers on localization accuracy. The effectiveness and potential of the proposed filter are verified by lake trials.

Automated summary

This study proposes a novel robust delay filtering algorithm for the cooperative localization of AUVs to cope with time-varying delay and non-Gaussian noise caused by outliers. The algorithm introduces statistical similarity measure to construct the cost function and successfully alleviates the impacts of delayed measurement with outliers on localization accuracy, as verified through lake trials.