An efficient sensor quantization algorithm for decentralized estimation fusion

Automatica ◽  
2011 ◽  
Vol 47 (5) ◽  
pp. 1053-1059 ◽  
Author(s):  
Xiaojing Shen ◽  
Yunmin Zhu ◽  
Zhisheng You
Keyword(s):  
Decentralized Estimation ◽  
Estimation Fusion

scholarly journals Decentralized Estimation of Two-port Equivalents for Power Transmission Lines

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Vedanta Pradhan ◽  
O. D. Naidu ◽  
Sinisa Zubic ◽  
Patrick Cost
Keyword(s):  
Transmission Lines ◽  
Power Transmission ◽  
Power Transmission Lines ◽  
Decentralized Estimation

scholarly journals Three State Estimation Fusion Methods Based on the Characteristic Function Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1440
Author(s):  
Yiran Yuan ◽  
Chenglin Wen ◽  
Yiting Qiu ◽  
Xiaohui Sun
Keyword(s):  
Characteristic Function ◽  
State Estimation ◽  
Packet Loss ◽  
Estimation Accuracy ◽  
Measurement Systems ◽  
Fusion Methods ◽  
Estimation Fusion ◽  
Parallel Filter ◽  
Sequential Filter ◽  
Better Than

There are three state estimation fusion methods for a class of strong nonlinear measurement systems, based on the characteristic function filter, namely the centralized filter, parallel filter, and sequential filter. Under ideal communication conditions, the centralized filter can obtain the best state estimation accuracy, and the parallel filter can simplify centralized calculation complexity and improve feasibility; in addition, the performance of the sequential filter is very close to that of the centralized filter and far better than that of the parallel filter. However, the sequential filter can tolerate non-ideal conditions, such as delay and packet loss, and the first two filters cannot operate normally online for delay and will be invalid for packet loss. The performance of the three designed fusion filters is illustrated by three typical cases, which are all better than that of the most popular Extended Kalman Filter (EKF) performance.

Decentralized Fixed-Interval Smoothing Algorithms

1986 ◽  
Vol 108 (1) ◽  
pp. 86-89 ◽  
Author(s):  
Keigo Watanabe
Keyword(s):  
Computational Efficiency ◽  
A Priori ◽  
Superposition Principle ◽  
Fixed Interval ◽  
Central Processor ◽  
Decentralized Estimation ◽  
Information Filter

The Weineret-Desai smoother formula is applied to derive new decentralized fixed-interval smoothing algorithms for a decentralized estimation structure consisting of a central processor and of M local processors. Such algorithms are based on decentralizing the estimates of global backward information filter and obtained from the use of the superposition principle in scattering framework. The smoothing update problem is also investigated to illustrate the application of the proposed algorithms. The emphasis is on computational efficiency, independence of local a priori statistics, and flexibility of implementation.

Convex Linear Estimation Fusion

2012 ◽  
pp. 191-240
Author(s):  
Yunmin Zhu ◽  
Jie Zhou ◽  
Xiaojing Shen ◽  
Enbin Song ◽  
Yingting Luo
Keyword(s):  
Linear Estimation ◽  
Estimation Fusion
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