Automatic Relevance Determination Kernel-Embedded Gaussian Process Regression for Sonar-Based Human Leg Localization with a Mobile Robot

Human leg localization problems involving sonar sensing can be posed as a
nonlinear regression problem
, and, nonparametric Bayesian methods, such as the
Gaussian process regression
(GPR) model, are potential solution candidates. In this work, to overcome the problem of irrelevant input features from the sonar range data, an advanced
automatic relevance determination
kernel structure is proposed to be used in the GPR model instead of the commonly used standard isotropic kernel. It is able to extract high-relevance input features even from partially trained data, thus offering a better generalization ability while improving the prediction rates and robustness significantly.