Multisensor estimation fusion with gaussian process for nonlinear dynamic systems

Abstract

The Gaussian process is gaining increasing importance in different areas such as signal processing, machine learning, robotics, control and aerospace and electronic systems, since it can represent unknown system functions by posterior probability. This paper investigates multisensor fusion in the setting of Gaussian process estimation for nonlinear dynamic systems. In order to overcome the difficulty caused by the unknown nonlinear system models, we associate the transition and measurement functions with the Gaussian process regression models, then the advantages of the non-parametric feature of the Gaussian process can be fully extracted for state estimation. Next, based on the Gaussian process filters, we propose two different fusion methods, centralized estimation fusion and distributed estimation fusion, to utilize the multisensor measurement information. Furthermore, the equivalence of the two proposed fusion methods is established by rigorous analysis. Finally, numerical examples for nonlinear target tracking systems demonstrate the equivalence and show that the multisensor estimation fusion performs better than the single sensor. Meanwhile, the proposed fusion methods outperform the convex combination method and the relaxed Chebyshev center covariance intersection fusion algorithm.