Distributed Kalman filtering for time-varying discrete sequential systems

Automatica ◽  
2019 ◽  
Vol 99 ◽  
pp. 228-236 ◽  
Author(s):  
Bo Chen ◽  
Guoqiang Hu ◽  
Daniel W.C. Ho ◽  
Li Yu
Keyword(s):  
Kalman Filtering ◽  
Time Varying ◽  
Sequential Systems ◽  
Distributed Kalman Filtering

Fusion Techniques Using Distributed Kalman Filtering for Detecting Changes in Systems

1991 ◽  
Author(s):  
Celeste M. Belcastro ◽  
Robert Fischl ◽  
Moshe Kam
Keyword(s):  
Kalman Filtering ◽  
Distributed Kalman Filtering

Leader-following-based distributed Kalman filtering in sensor networks with communication delay

2017 ◽  
Vol 354 (16) ◽  
pp. 7504-7520 ◽  
Author(s):  
Ya Zhang ◽  
Feifei Li ◽  
Yangyang Chen
Keyword(s):  
Sensor Networks ◽  
Kalman Filtering ◽  
Communication Delay ◽  
Leader Following ◽  
Distributed Kalman Filtering

A majorization inequality and its application to distributed Kalman filtering

Automatica ◽  
2011 ◽  
Vol 47 (11) ◽  
pp. 2438-2443 ◽  
Author(s):  
Simone Del Favero ◽  
Sandro Zampieri
Keyword(s):  
Kalman Filtering ◽  
Distributed Kalman Filtering

scholarly journals A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 880
Author(s):  
Mohammad R. Rezaei ◽  
Milos R. Popovic ◽  
Milad Lankarany
Keyword(s):  
Kalman Filtering ◽  
Probability Distributions ◽  
Neural Code ◽  
Time Varying ◽  
Amount Of Information ◽  
Neural Ensemble ◽  
Different Types ◽  
Types Of Information ◽  
Different Time Scales ◽  
Filtering Approach

The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is associated with different features of the stimulus. By calculating a neural ensemble’s information in response to a mixed stimulus comprising slow and fast signals, we show that the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code.

scholarly journals Distributed Kalman Filtering Over Sensor Networks With Unknown Random Link Failures

2018 ◽  
Vol 2 (4) ◽  
pp. 587-592 ◽  
Author(s):  
Stefano Battilotti ◽  
Filippo Cacace ◽  
Massimiliano d'Angelo ◽  
Alfredo Germani
Keyword(s):  
Sensor Networks ◽  
Kalman Filtering ◽  
Link Failures ◽  
Distributed Kalman Filtering

Estimation of time‐varying signal parameters via VFM Kalman filtering

1999 ◽  
Vol 105 (2) ◽  
pp. 1309-1310
Author(s):  
Sang‐Wook Lee ◽  
Jun‐Seok Lim ◽  
Byung‐Doo Jun ◽  
Koeng‐Mo Sung
Keyword(s):  
Kalman Filtering ◽  
Time Varying ◽  
Signal Parameters ◽  
Estimation Of Time

A new distributed Kalman filtering based on means-quare estimation upper bounds

2018 ◽  
Vol 355 (9) ◽  
pp. 4032-4049 ◽  
Author(s):  
Zhipeng Li ◽  
Minyue Fu ◽  
Huanshui Zhang
Keyword(s):  
Kalman Filtering ◽  
Upper Bounds ◽  
Distributed Kalman Filtering
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