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Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission
Raquel Caballero-Águila1, Aurora Hermoso-Carazo2, Josefa Linares-Pérez3
1Dpto. de Estadística, Universidad de Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain. raguila@ujaen.es.
Sensors (Basel, Switzerland)
|May 20, 2017
Summary
This study introduces optimal fusion estimators for networked systems facing random delays and packet losses. The developed recursive algorithms enhance estimation accuracy without needing signal models or knowing specific delay occurrences.
Area of Science:
- Control Systems Engineering
- Networked Systems
- Estimation Theory
Background:
- Networked stochastic systems are prone to data transmission issues like random delays and packet dropouts.
- Sensor measurements are often corrupted by random parameter matrices and cross-correlated noise.
- Accurate state estimation is crucial for effective system control and decision-making.
Purpose of the Study:
- To develop optimal fusion estimation algorithms for networked stochastic systems.
- To address challenges posed by bounded random delays and packet dropouts in data transmission.
- To design estimators that are robust to uncertainties in sensor measurements.
Main Methods:
- Design of least-squares fusion linear estimators (filter, predictor, fixed-point smoother) using the innovation analysis approach.
- Development of recursive algorithms that utilize delay probabilities but do not require knowledge of individual delayed measurements.
- Modeling sensor outputs perturbed by random parameter matrices and cross-correlated white additive noises.
Main Results:
- Successfully designed optimal fusion linear estimators and their corresponding error covariance matrices.
- The proposed recursive algorithms are independent of the signal evolution model, relying only on first and second-order moments.
- Analysis of practical scenarios with random parameter matrices demonstrates the influence of delays on estimation accuracy.
Conclusions:
- The developed fusion estimation methods provide robust solutions for networked systems with data transmission uncertainties.
- The algorithms offer a practical approach by not requiring knowledge of specific delay instances or the signal model.
- The study highlights the significant impact of random delays on estimation performance, validated through numerical examples.
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