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This paper analyses the centralized fusion linear estimation problem inmulti-sensor systems with multiple packet dropouts and correlated noises.Packet dropouts are modeled by independent Bernoulli distributed randomvariables. This problem is addressed in the tessarine domain under conditionsof T1 and T2-properness, which entails a reduction in the dimension of theproblem and, consequently, computational savings. The methodology proposedenables us to provide an optimal (in the least-mean-squares sense) linearfusion filtering algorithm for estimating the tessarine state with a lowercomputational cost than the conventional one devised in the real field.Simulation results illustrate the performance and advantages of the solutionproposed in different settings.

An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts