Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

T-proper Hypercomplex Centralized Fusion Estimation for Randomly Multiple Sensor Delays Systems with Correlated Noises

Version 1 : Received: 15 July 2021 / Approved: 16 July 2021 / Online: 16 July 2021 (16:30:57 CEST)

How to cite: Fernández-Alcalá, R.M.; Navarro-Moreno, J.; Ruiz-Molina, J.C. T-proper Hypercomplex Centralized Fusion Estimation for Randomly Multiple Sensor Delays Systems with Correlated Noises. Preprints 2021, 2021070389 (doi: 10.20944/preprints202107.0389.v1). Fernández-Alcalá, R.M.; Navarro-Moreno, J.; Ruiz-Molina, J.C. T-proper Hypercomplex Centralized Fusion Estimation for Randomly Multiple Sensor Delays Systems with Correlated Noises. Preprints 2021, 2021070389 (doi: 10.20944/preprints202107.0389.v1).

Abstract

The centralized fusion estimation problem for discrete-time vectorial tessarine signals in multiple sensor stochastic systems with random one-step delays and correlated noises is analyzed under different T-properness conditions. Based on Tk, k=1,2, linear processing, new centralized fusion filtering, prediction, and fixed-point smoothing algorithms are devised. These algorithms have the advantage of providing optimal estimators with a significant reduction in computational cost compared to that obtained through a real or widely linear processing approach. Simulation examples illustrate the effectiveness and applicability of the algorithms proposed, in which the superiority of the Tk linear estimators over their counterparts in the quaternion domain is apparent.

Subject Areas

Centralized fusion estimation, Random delay systems, Tessarine processing, Tk properness.

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