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Distributed Kalman Filtering Over Sensor Networks With Transmission Delays
IEEE Transactions on Cybernetics
|April 11, 2020
Summary
This study addresses distributed state estimation in sensor networks with time-varying delays. A Kalman consensus filtering algorithm ensures accurate state estimation and error convergence in distributed systems.
Area of Science:
- Control Engineering
- Networked Systems
- Signal Processing
Background:
- Sensor networks are crucial for distributed data acquisition.
- Communication links in these networks can experience unpredictable transmission delays.
- Accurate state estimation is vital for effective network operation.
Purpose of the Study:
- To develop a distributed state estimation algorithm for sensor networks.
- To account for bounded time-varying transmission delays in communication links.
- To ensure the convergence of estimation errors and boundedness of error covariances.
Main Methods:
- A distributed Kalman filtering algorithm was designed.
- The algorithm is based on a Kalman consensus filtering approach.
- Sufficient conditions for error convergence and covariance boundedness were derived.
Main Results:
- The designed distributed Kalman filtering algorithm effectively estimates states in sensor networks.
- Convergence of estimation errors was proven under specified conditions.
- The boundedness of error covariances was mathematically established.
Conclusions:
- The proposed distributed Kalman filtering algorithm is effective for state estimation in sensor networks with transmission delays.
- The derived conditions guarantee the stability and reliability of the estimation process.
- Simulation results validate the algorithm's performance.
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