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This paper investigates an issue of distributed fusion estimation undernetwork-induced complexity and stochastic parameter uncertainties. First, anovel signal selection method based on event-trigger is developed to handlenetwork-induced packet dropouts as well as packet disorders resulting fromrandom transmission delays, where the ${H_2}/{H_\infty }$ performance of thesystem is analyzed in different noise environments. In addition, a linear delaycompensation strategy is further employed for solving the complexitynetwork-induced problem, which may deteriorate the system performance.Moreover, a weighted fusion scheme is used to integrate multiple resourcesthrough an error cross-covariance matrix. Several case studies validate theproposed algorithm and demonstrate satisfactory system performance in targettracking.

Distributed Fusion Estimation for Stochastic Uncertain Systems with Network-Induced Complexity and Multiple Noise