Title: Two-level robust sequential covariance intersection fusion Kalman predictors over clustering sensor networks with uncertain noise variances
Authors: Wen Juan Qi; Peng Zhang; Zi Li Deng
Addresses: Department of Automation, Heilongjiang University, Harbin 150080, China ' Department of Automation, Heilongjiang University, Harbin 150080, China ' Department of Automation, Heilongjiang University, Harbin 150080, China
Abstract: This paper studies the problem of designing two-level robust sequential covariance intersection (SCI) fusion Kalman predictors for the clustering sensor networks with noise variances uncertainties. The sensor networks consist of many clusters, which are partitioned by the nearest neighbour rule. According to the minimax robust estimation principle, based on the worst-case conservative clustering sensor network with the conservative upper bound of noise variances, the two-level SCI fusion Kalman predictors are presented where the first level is the local SCI fusion predictors and the second level is the global SCI fusion predictor. This two-level fused structure can significantly reduce the communicational burden and save the energy sources. The robustness of the local and fused Kalman predictors is proved based on the Lyapunov equation method, and the robust accuracy relations are proved. A simulation example verifies the correctness and effectiveness of the proposed robust SCI predictor.
Keywords: sensor networks; sequential covariance intersection; SCI fusion; robust Kalman predictor; robust accuracy; uncertain noise variance; minimax robust estimation; simulation; clustering.
International Journal of Sensor Networks, 2013 Vol.14 No.4, pp.251 - 261
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 03 Feb 2014 *