Kernel-function-based models for acoustic localization of underwater vehicles - Pinheiro, Breno C. (2019)
This paper proposes a novel design for the localization system of autonomous underwater vehicles (AUVs) using acoustic signals. The solution presented exploits models based on kernel functions with two main purposes: 1) to reject outliers; and 2) to correct or improve accuracy of measurements. The localization system discussed is based on well-established techniques such as support vector data description (SVDD) and autoassociative kernel regression (AAKR) derived from machine learning theory that utilizes heuristic models for classification and regression tasks, respectively. By coupling the algorithm to the navigation system, we seek to reduce the sensitivity of the localization scheme to the reflected acoustic waves or fluctuations of underwater channel properties without modifying the solution used for data fusion or overloading the algorithm embedded in the vehicle. Data collected in the field with a light underwater vehicle (LAUV) were used to demonstrate the advantages of the proposed approach.