Robust Estimation Fusion in Wireless Senor Networks with Outliers and Correlated Noises

This paper addresses the problem of estimation fusion in a distributed wireless sensor network (WSN) under the following conditions: (i) sensor noises are contaminated by outliers or gross errors; (ii) process noise and sensor noises are correlated; (iii) cross-correlation among local estimates is unknown. First, to attack the correlation and outliers, a correlated robust Kalman filtering (coR 2 KF) scheme with weighted matrices on innovation sequences is introduced as local estimator. It is shown that the proposed coR 2 KF takes both conventional Kalman filter and robust Kalman filter as a special case. Then, a novel version of our internal ellipsoid approximation fusion (IEAF) is used in the fusion center to handle the unknown cross-correlation of local estimates. The explicit solution to both fusion estimate and corresponding covariance is given. Finally, to demonstrate robustness of the proposed coR 2 KF and the effectiveness of IEAF strategy, a simulation example of tracking a target moving on noisy circular trajectories is included.