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\({\mathbb{T}}\)-Proper Hypercomplex Centralized Fusion Estimation for Randomly Multiple Sensor Delays Systems with
Rosa M Fernández-Alcalá1, Jesús Navarro-Moreno1, Juan C Ruiz-Molina1
1Department of Statistics and Operations Research, University of Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain.
Sensors (Basel, Switzerland)
|September 10, 2021
Summary
New algorithms for centralized fusion estimation of tessarine signals improve accuracy and reduce computational cost in stochastic systems with delays and noise. These Tk linear estimators outperform quaternion-based methods.
Area of Science:
- Signal Processing
- Estimation Theory
- Stochastic Systems
Background:
- Centralized fusion estimation is crucial for multi-sensor systems.
- Stochastic systems with delays and correlated noises present significant challenges.
- T-properness conditions are key to analyzing complex signal properties.
Purpose of the Study:
- To develop novel centralized fusion estimation algorithms for discrete-time vectorial tessarine signals.
- To address systems with random one-step delays and correlated noises.
- To reduce computational cost while maintaining optimal estimation performance.
Main Methods:
- Analysis under different T-properness conditions.
- Development of Tk, k=1,2, linear processing algorithms.
- Design of centralized fusion filtering, prediction, and fixed-point smoothing algorithms.
Main Results:
- New algorithms provide optimal estimators.
- Significant reduction in computational cost compared to real or widely linear processing.
- Demonstrated effectiveness and applicability through simulation examples.
Conclusions:
- The proposed Tk linear estimators offer superior performance over quaternion-based methods.
- The developed algorithms provide an efficient solution for centralized fusion estimation problems.
- This work advances the field of signal processing in complex stochastic environments.
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