Consensus‐based nonlinear smoother for distributed continuous‐discrete systems

Abstract

AbstractIn order to estimate the state of general distributed continuous‐discrete nonlinear system, a new consensus on the measurement and information cubature smoothing method is proposed in this article. The distributed continuous‐discrete nonlinear dynamic system refers to a system whose process is described as a stochastic differential equation (SDE) and whose measurements are provided by a wireless sensor network (WSN) at discrete times. First, the cubature rule is embedded into the consensus on measurements and information extended Kalman filter (CMIEKF) architecture by using the statistical linear regression (SLR) method, and then the presented consensus‐based method is obtained according to the Rauch–Tung–Striebel (RTS) smoothing methodology. In a numerical simulation, the performances of the presented smoothing method and the conventional nonlinear estimating methods are compared and analyzed. The numerical results show that, compared with other distributed estimation methods, the newly proposed smoothing algorithm enjoys not only higher accuracy but also stronger robustness.