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

In this paper, the recursive filtering problem is investigated for the nonlinear time-varying systems with the collaborative prediction algorithm (CPA) under a low-duty-cycle wireless transmission mechanism. To significantly save energy and bandwidth resources, low-duty-cycle scheduling (LDCS) is em...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on signal processing Ročník 72; s. 1 - 13
Hlavní autori: Gao, Hongyu, Yu, Lindong, Hou, Nan, Song, Jinbo, Li, Yue
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Collaboration
Collaborative prediction algorithm
Covariance
Covariance matrices
Filtration
low-duty-cycle scheduling
Nonlinear systems
nonlinear time-varying systems
Performance degradation
Petroleum
Prediction algorithms
recursive filtering
Resource scheduling
Scheduling
Signal processing algorithms
Time measurement
Time-varying systems
Wireless sensor networks
ISSN:1053-587X, 1941-0476
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In this paper, the recursive filtering problem is investigated for the nonlinear time-varying systems with the collaborative prediction algorithm (CPA) under a low-duty-cycle wireless transmission mechanism. To significantly save energy and bandwidth resources, low-duty-cycle scheduling (LDCS) is employed in practical engineering. Under this communication scheduling, the sensor nodes are allowed to remain in dormant states for a relatively long period of time. 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2023.3343558