  This paper investigates an issue of distributed fusion estimation under
network-induced complexity and stochastic parameter uncertainties. First, a
novel signal selection method based on event-trigger is developed to handle
network-induced packet dropouts as well as packet disorders resulting from
random transmission delays, where the ${H_2}/{H_\infty }$ performance of the
system is analyzed in different noise environments. In addition, a linear delay
compensation strategy is further employed for solving the complexity
network-induced problem, which may deteriorate the system performance.
Moreover, a weighted fusion scheme is used to integrate multiple resources
through an error cross-covariance matrix. Several case studies validate the
proposed algorithm and demonstrate satisfactory system performance in target
tracking.
