Distributed State Estimation With Colored Noises
Yue Luo, Jiayu Zhou, Wen Yang
Abstract
Most of the existing studies mainly consider the process and measurement noise as Gaussian white noises, while a large number of systems are influenced by colored noises in the practical applications. In this brief, a distributed state estimator with colored noises is proposed. Since every moment of colored noise sequence is related, we first make the colored noises equivalent to Gaussian white noises by augmenting the state matrix. Then, we design a state estimator with colored noises, and further analyze the convergence property of the estimation error covariance. Ultimately, simulation results indicate that the designed distributed state estimator can effectively address colored noises and ensure high accuracy and performance of state estimation, and demonstrate the relationship between colored noise parameters and estimation performance as well.