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Distributed state estimation for discrete-time sensor networks with randomly varying nonlinearities and missing
Jinling Liang1, Zidong Wang, Xiaohui Liu
1Department of Mathematics, Southeast University, Nanjing 210096, China. jinlliang@gmail.c
IEEE Transactions on Neural Networks
|February 24, 2011
Summary
This study develops distributed state estimators for sensor networks with random nonlinearities and missing data. The new method ensures accurate state approximation despite system uncertainties and communication failures.
Area of Science:
- Control Systems Engineering
- Networked Systems
- Signal Processing
Background:
- Sensor networks often face challenges like nonlinearities and data loss.
- Distributed estimation is crucial when centralized processing is infeasible.
- Stochastic systems with Brownian motion introduce inherent uncertainties.
Purpose of the Study:
- To design distributed state estimators for discrete-time stochastic systems.
- To address randomly varying nonlinearities and missing measurements in sensor networks.
- To ensure reliable state estimation in decentralized network architectures.
Main Methods:
- Developing distributed algorithms for state estimation.
- Utilizing neighboring sensor measurements based on network topology.
- Analyzing convergence of estimation error under stochastic disturbances.
Main Results:
- Sufficient conditions for estimation error convergence are established.
- Explicit expressions for individual distributed estimators are derived.
- The proposed method accounts for nonlinearities and missing data.
Conclusions:
- The developed distributed state estimators effectively approximate system states.
- The approach provides a robust solution for realistic sensor network conditions.
- Theoretical results are validated through a numerical example.
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