Fusion algorithm of correlated local estimates

Three algorithms for fusing local estimates are compared. The first one (algorithm A) is the well known Federated filtering algorithm proposed by Carlson [Federated filter for fault-tolerant integrated navigation systems, in: Proceedings of IEEE Position, Location and Navigation Symposium, Oriando, FL, 1988 pp. 110-119; IEEE Trans. Aerospace and Electronic System 26 (3) (1990) 517-525], which needs an Upper Bound technique to eliminate the con-elation between local estimates, and a reset procedure to make the global estimate optimal. The second one (algorithm B) proposed by Hong Jin and Hong Yue Zhang directly calculates the optimal global estimate as a weighted sum of correlated local estimates using general weighting matrices [Fusion algorithm of correlated local estimates for federated filter, in: Proceedings of the 3rd Asian Control Conference, Shanghai, 2000, pp. 1428-1433]. In this paper a simplified algorithm (algorithm C) is derived, which uses diagonal weighting matrices. The simplification leads to less computation as compared to that of algorithm B, but the Global estimate is sub-optimal. Comparison between these three algorithms is conducted by theoretical analysis and extensive simulations as well. The comparison reveals that the algorithm C has moderate calculation load, strong fault tolerance and little loss in estimation accuracy. And the sensitivities to the values of covariance, matrices of noises are similar for the three algorithms. (C) 2004 Elsevier SAS. All rights reserved.