Collaborative-Prediction-Based Recursive Filtering for Nonlinear Systems Subject to Low-Duty-Cycle Scheduling

The objective of this study is to design a filtering scheme that can ensure the filtering performance for the nonlinear systems under the LDCS. To solve the problem of filtering performance degradation due to high data sparsity caused by the low duty cycle, the CPA combined with the zero-order holder (ZOH) is introduced into the filtering scheme. The desired gain matrix is first computed recursively by minimizing the obtained filtering error covariance upper matrix. Next, the boundedness of the filtering error covariance is discussed. Finally, the developed filtering approach based on the CPA and ZOH under the low-duty-cycle scheduling is verified by a simulation case for its effectiveness.
Source: IEEE Transactions on Signal Processing - Category: Biomedical Engineering Source Type: research