Robust information filter for decentralized estimation

Automatica ◽  
2005 ◽  
Vol 41 (12) ◽  
pp. 2141-2146 ◽  
Author(s):  
Ying Zhang ◽  
Yeng Chai Soh ◽  
Weihai Chen
Keyword(s):  
Decentralized Estimation ◽  
Information Filter

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.

scholarly journals Sdgan: Improve Speech Enhancement Quality by Information Filter

2021 ◽  
Vol 1871 (1) ◽  
pp. 012063
Author(s):  
Xiaozhou Guo ◽  
Yi Liu ◽  
Wenyu Mao ◽  
Jixing Li ◽  
Wenchang Li ◽  
...  
Keyword(s):  
Speech Enhancement ◽  
Information Filter

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 A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 743
Author(s):  
Xi Liu ◽  
Shuhang Chen ◽  
Xiang Shen ◽  
Xiang Zhang ◽  
Yiwen Wang
Keyword(s):  
State Estimation ◽  
Measurement Model ◽  
High Dimensional ◽  
Neural Firing ◽  
The Neural Network ◽  
Information Filter ◽  
Critical Technology ◽  
Dimensional Measurements ◽  
Non Gaussian ◽  
Low Dimensional

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.

Improved square-root cubature information filter

2016 ◽  
Vol 39 (4) ◽  
pp. 579-588 ◽  
Author(s):  
Yulong Huang ◽  
Yonggang Zhang ◽  
Ning Li ◽  
Lin Zhao
Keyword(s):  
Large Scale ◽  
Estimation Error ◽  
Sampling Interval ◽  
Prediction Errors ◽  
Square Root ◽  
Sigma Point ◽  
Theoretical Comparison ◽  
Error Covariance ◽  
Information Filter ◽  
Scale Matrix

In this paper, a theoretical comparison between existing the sigma-point information filter (SPIF) framework and the unscented information filter (UIF) framework is presented. It is shown that the SPIF framework is identical to the sigma-point Kalman filter (SPKF). However, the UIF framework is not identical to the classical SPKF due to the neglect of one-step prediction errors of measurements in the calculation of state estimation error covariance matrix. Thus SPIF framework is more reasonable as compared with UIF framework. According to the theoretical comparison, an improved cubature information filter (CIF) is derived based on the superior SPIF framework. Square-root CIF (SRCIF) is also developed to improve the numerical accuracy and stability of the proposed CIF. The proposed SRCIF is applied to a target tracking problem with large sampling interval and high turn rate, and its performance is compared with the existing SRCIF. The results show that the proposed SRCIF is more reliable and stable as compared with the existing SRCIF. Note that it is impractical for information filters in large-scale applications due to the enormous computational complexity of large-scale matrix inversion, and advanced techniques need to be further considered.

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
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