High-Rate Underwater Acoustic Localization Based on the Decision Tree
Sibo Sun, Tieyuan Liu, Yilin Wang, Guangpu Zhang, Kaixin Liu, Yong Wang
Abstract
Underwater acoustic localization (UWAL) is widely applied in ocean exploration, and a key issue for the high-rate time-of-arrival (TOA)-based UWAL method is to select the direct signal in the multipath acoustic channel. However, the existing direct signal selection methods suffer from a deterioration of the selection accuracy since the received signals from adjacent periods are mixed with each other. Addressing the problem, we propose a high-rate direct signal selection algorithm in this article. First, we choose the amplitude, TOA, bandwidth, and Doppler frequency of the detected pulses as the input features. Second, a direct signal classifier is established utilizing the decision tree. Finally, the localization model is built based on the TDOA information of the direct signal, and the localization precision analysis evaluates the performance of the UWAL method. Sufficient experiments, including simulation, tank experiment, and sea trial, are implemented to verify the effectiveness of the proposed method.