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Networked Fusion Filtering from Outputs with Stochastic Uncertainties and Correlated Random Transmission Delays
Raquel Caballero-Águila1, Aurora Hermoso-Carazo2, Josefa Linares-Pérez3
1Departamento de Estadística, Universidad de Jaén, Campus Las Lagunillas, 23071 Jaén, Spain. raguila@ujaen.es.
Sensors (Basel, Switzerland)
|June 25, 2016
Summary
This study develops optimal linear filters for sensor networks facing random transmission delays and uncertainties. Recursive algorithms are derived for both distributed and centralized fusion filtering, enhancing system accuracy.
Area of Science:
- Control Systems Engineering
- Signal Processing
- Networked Systems
Background:
- Sensor networks are crucial for data acquisition but face challenges like random transmission delays and measurement uncertainties.
- Existing filtering methods often require complete knowledge of signal evolution models, limiting their applicability.
Purpose of the Study:
- To develop robust recursive algorithms for distributed and centralized fusion filtering in sensor networks.
- To address challenges posed by random one-step delays and parameter uncertainties in measurements.
- To derive optimal linear filters under the least-squares criterion without needing the signal evolution model.
Main Methods:
- Utilized an innovation approach to derive recursive algorithms for optimal linear filters.
- Developed local estimators based on individual sensor measurements.
- Generated a distributed fusion filter as a matrix-weighted combination of local estimators.
- Proposed a recursive algorithm for the optimal linear centralized filter.
- Derived recursive formulas for error covariance matrices to assess filter performance.
Main Results:
- Successfully derived recursive algorithms for both distributed and centralized fusion filters.
- Demonstrated that the filters can operate without prior knowledge of the signal evolution model, using only moment information.
- Quantified the impact of random delays and network-induced uncertainties on filter accuracy through derived covariance formulas.
- Validated the approach with a numerical example showcasing the handling of network-induced uncertainties.
Conclusions:
- The proposed filtering algorithms provide optimal linear solutions for sensor networks with random delays and uncertainties.
- The method offers a unified framework for addressing various network-induced phenomena using random matrices.
- The derived recursive formulas enable performance analysis and comparison of the developed estimators.
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