Distributed Gaussian Process Regression Under Localization Uncertainty

Abstract

In this paper, we propose distributed Gaussian process regression (GPR) for resourceconstrained distributed sensor networks under localization uncertainty. The proposed distributed algorithm, which combines Jacobi overrelaxation (JOR) and discretetime average consensus (DAC), can effectively handle localization uncertainty as well as limited communication and computation capabilities of distributed sensor networks. We also extend the proposed method hierarchically using sparse GPR to improve its scalability. The performance of the proposed method is verified in numerical simulations against the centralized maximum a posteriori (MAP) solution and a quickanddirty solution. We show that the proposed method outperforms the quickanddirty solution and achieve an accuracy comparable to the centralized solution.