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

TL;DR

This paper presents an optimal linear fusion estimation algorithm for T-proper multi-sensor systems with packet dropouts, reducing computational complexity while maintaining estimation accuracy.

Contribution

It introduces a dimension-reduction approach in the tessarine domain for optimal linear fusion filtering under packet dropouts and correlated noises.

Findings

The proposed algorithm achieves lower computational cost than conventional methods.

Simulation results confirm the effectiveness and advantages of the approach.

The method is applicable to systems with multiple packet dropouts and T-properness conditions.

Abstract

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