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A Two-Phase Distributed Filtering Algorithm for Networked Uncertain Systems with Fading Measurements under Deception
Raquel Caballero-Águila1, Aurora Hermoso-Carazo2, Josefa Linares-Pérez2
1Departamento de Estadística, Universidad de Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain.
Sensors (Basel, Switzerland)
|November 14, 2020
Summary
This study presents a new distributed filtering method for discrete-time stochastic systems facing deception attacks. The approach enhances state estimation accuracy in networked sensor systems despite various real-world imperfections.
Area of Science:
- Control Systems Engineering
- Networked Systems
- Signal Processing
Background:
- Distributed filtering is crucial for networked systems.
- Sensor networks are vulnerable to deception attacks and environmental imperfections.
- Existing methods may not adequately address correlated noises and fading measurements.
Purpose of the Study:
- To develop a robust distributed filtering algorithm for discrete-time stochastic systems.
- To account for deception attacks with probabilistic success.
- To integrate system imperfections like fading measurements and multiplicative/additive noises.
Main Methods:
- A recursive least-squares linear filter is designed using local and adjacent sensor measurements.
- A distributed filter is obtained by fusing adjacent sensors' estimates.
- The fusion strategy minimizes the mean squared error using matrix weighting.
Main Results:
- The proposed algorithm effectively estimates the system state in the presence of deception attacks.
- The distributed filter demonstrates improved performance compared to single-sensor approaches.
- Simulation results validate the strategy's efficiency in reducing error variances.
Conclusions:
- The developed distributed filtering strategy is effective for networked systems under attack.
- The method robustly handles various system imperfections and noise correlations.
- This approach offers a promising solution for secure and accurate state estimation in sensor networks.
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