Variable forgetting factor mechanisms for diffusion recursive least squares algorithm in sensor networks

In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrum estimation in sensor networks. For the proposed algorithms,...

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Veröffentlicht in:EURASIP journal on advances in signal processing Jg. 2017; H. 1; S. 1 - 23
Hauptverfasser: Zhang, Ling, Cai, Yunlong, Li, Chunguang, de Lamare, Rodrigo C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 15.08.2017
Springer
Springer Nature B.V
SpringerOpen
Schlagworte:
Algorithms
Analysis
Complexity
Computer simulation
Diffusion recursive least-squares
Distributed parameter estimation
Distributed spectrum estimation
Economic models
Engineering
Error analysis
Exact solutions
Least squares method
Mathematical analysis
Mean square values
Quantum Information Technology
Sensor networks
Sensors
Signal,Image and Speech Processing
Spintronics
Variable forgetting factor
Sensor networks
Distributed parameter estimation
Variable forgetting factor
Diffusion recursive least-squares
Distributed spectrum estimation
ISSN:1687-6180, 1687-6172, 1687-6180
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Beschreibung
Zusammenfassung:In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrum estimation in sensor networks. For the proposed algorithms, they can adjust the forgetting factor automatically according to the posteriori error signal. We develop detailed analyses in terms of mean and mean square performance for the proposed algorithms and derive mathematical expressions for the mean square deviation (MSD) and the excess mean square error (EMSE). The simulation results show that the proposed low-complexity VFF-DRLS algorithms achieve superior performance to the existing DRLS algorithm with fixed forgetting factor when applied to scenarios of distributed parameter and spectrum estimation. Besides, the simulation results also demonstrate a good match for our proposed analytical expressions.
Bibliographie:ObjectType-Article-1
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-017-0490-z