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Recursive Filtering Under Probabilistic Encoding-Decoding Schemes: Handling Randomly Occurring Measurement Outliers
IEEE Transactions on Cybernetics
|April 6, 2023
Summary
This study introduces a new recursive filtering algorithm to handle randomly occurring measurement outliers (ROMOs) in networked systems. The method effectively removes corrupted data, ensuring robust filtering performance and accurate system state estimation.
Area of Science:
- Control Systems Engineering
- Signal Processing
- Stochastic Systems
Background:
- Networked systems are susceptible to performance degradation due to measurement outliers.
- Randomly occurring measurement outliers (ROMOs) pose a significant challenge in recursive filtering.
- Existing filtering methods may struggle with the dynamic and unpredictable nature of ROMOs.
Purpose of the Study:
- To develop a novel recursive filtering algorithm for time-varying systems with ROMOs.
- To accurately model the dynamical behaviors of ROMOs using stochastic methods.
- To preserve filtering performance by actively detecting and removing outlier-corrupted measurements.
Main Methods:
- A new model for ROMOs using independent and identically distributed stochastic scalars.
- A probabilistic encoding-decoding scheme for measurement signal conversion.
- An active detection-based method to remove problematic measurements.
- A recursive calculation approach to derive time-varying filter parameters by minimizing the filtering error covariance upper bound.
- Stochastic analysis techniques to analyze the uniform boundedness of the filtering error covariance.
Main Results:
- A novel recursive filtering algorithm designed to mitigate the impact of ROMOs.
- The proposed algorithm effectively removes measurements contaminated by outliers.
- A time-varying filter parameter is derived recursively by minimizing the filtering error covariance.
- The uniform boundedness of the filtering error covariance is theoretically analyzed.
- Numerical examples demonstrate the effectiveness and correctness of the developed filter design.
Conclusions:
- The developed recursive filtering approach effectively addresses the challenge of ROMOs in networked time-varying systems.
- The active detection and removal of outlier measurements ensure robust and reliable filtering.
- The proposed method provides a significant advancement in maintaining filtering accuracy under noisy conditions.
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