Recursive filtering for complex networks using non‐linearly coupled UKF
Wenling Li, Cong Meng, Yingmin Jia, Junping Du · IET Control Theory and Applications · 2017
This study is concerned with the recursive filtering problem for a class of discrete‐time complex networks with non‐linearly coupled terms. A coupled unscented Kalman filter (UKF) is developed, where the sigma points of the UKF are propagated by introducing the coupled terms. By using the stochastic analysis technique, a sufficient condition is established to guarantee the boundedness of the estimation errors, where an upper bound for the coupling strength is derived. It is shown that the upper bound of the coupling strength is inversely proportional to the number of nodes. A numerical example involving tracking of multiple interacting targets is provided to verify the effectiveness of the proposed filter.