scholarly journals Event-triggered sensor data transmission policy for receding horizon recursive state estimation

2016 ◽  
Vol 11 (2) ◽  
pp. 178-185 ◽  
Author(s):  
Yunji Li ◽  
Li Peng
Keyword(s):  
State Estimation ◽  
Data Transmission ◽  
System Performance ◽  
Estimation Error ◽  
Sensor Data ◽  
Receding Horizon ◽  
Transmission Strategy ◽  
System A ◽  
Recursive State Estimation ◽  
Theoretical Results

We consider a sensor data transmission policy for receding horizon recursive state estimation in a networked linear system. A good tradeoff between estimation error and communication rate could be achieved according to a transmission strategy, which decides the transfer time of the data packet. Here we give this transmission policy through proving the upper bound of system performance. Moreover, the lower bound of system performance is further analyzed in detail. A numerical example is given to verify the potential and effectiveness of the theoretical results.

scholarly journals Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

2016 ◽  
Vol 14 (1) ◽  
pp. 934-945
Author(s):  
Cenker Biçer ◽  
Levent Özbek ◽  
Hasan Erbay
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Stochastic Systems ◽  
Estimation Error ◽  
The State ◽  
Forgetting Factor ◽  
The Matrix ◽  
Stochastic Framework ◽  
Theoretical Results

AbstractIn this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.The importance of the theoretical results and the contribution to estimation performance of the adaptation method are demonstrated interactively with the standard extended Kalman filter in the simulation part.

State Estimation Using Randomly Delayed Measurements

1993 ◽  
Vol 115 (1) ◽  
pp. 19-26 ◽  
Author(s):  
A. Ray ◽  
L. W. Liou ◽  
J. H. Shen
Keyword(s):  
State Estimation ◽  
Minimum Variance ◽  
The State ◽  
Sensor Data ◽  
State Estimator ◽  
Uniform Asymptotic Stability ◽  
Simulation Experiments ◽  
State Estimate ◽  
Sampling Instant ◽  
Recursive Relations

This paper presents a modification of the conventional minimum variance state estimator to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal. In this approach, the currently available sensor data is used at each sampling instant to obtain the state estimate which, in turn, can be used to generate the control signal. Recursive relations for the filter dynamics have been derived, and the conditions for uniform asymptotic stability of the filter have been conjectured. Results of simulation experiments using a flight dynamic model of advanced aircraft are presented for performance evaluation of the state estimation filter.

To err is system; a comparison of methodologies for the investigation of adverse outcomes in healthcare

2021 ◽  
pp. 251604352199026
Author(s):  
Peter Isherwood ◽  
Patrick Waterson
Keyword(s):  
Patient Safety ◽  
System Performance ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
Real Life ◽  
Adverse Outcomes ◽  
System Level ◽  
Future Performance ◽  
System A ◽  
Regulatory Bodies

Patient safety, staff moral and system performance are at the heart of healthcare delivery. Investigation of adverse outcomes is one strategy that enables organisations to learn and improve. Healthcare is now understood as a complex, possibly the most complex, socio-technological system. Despite this the use of a 20th century linear investigation model is still recommended for the investigation of adverse outcomes. In this review the authors use data gathered from the investigation of a real life healthcare near incident and apply three different methodologies to the analysis of this data. They compare both the methodologies themselves and the outputs generated. This illustrates how different methodologies generate different system level recommendations. The authors conclude that system based models generate the strongest barriers to improve future performance. Healthcare providers and their regulatory bodies need to embrace system based methodologies if they are to effectively learn from, and reduce future, adverse outcomes.

RECURSIVE STATE ESTIMATION ALGORITHM FOR ILL-CONDITIONED POWER SYSTEMS

1994 ◽  
Vol 22 (5) ◽  
pp. 583-592
Author(s):  
S. C. TRIPATHY ◽  
SUNITA CHOHAN ◽  
R. BALASUBRAMANIAN
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Estimation Algorithm ◽  
Recursive State Estimation

scholarly journals Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

AI Magazine ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 55 ◽  
Author(s):  
Nisarg Vyas ◽  
Jonathan Farringdon ◽  
David Andre ◽  
John Ivo Stivoric
Keyword(s):  
Machine Learning ◽  
Energy Expenditure ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Health Goals ◽  
System A ◽  
Multi Sensor Data Fusion ◽  
Learning Techniques ◽  
Insight Into

In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

scholarly journals On the Relation between Topological Entropy and Restoration Entropy

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 7 ◽  
Author(s):  
Christoph Kawan
Keyword(s):  
Dynamical Systems ◽  
State Estimation ◽  
Topological Entropy ◽  
Robust Estimation ◽  
Data Transmission ◽  
Communication Constraints ◽  
Dynamical Entropy

In the context of state estimation under communication constraints, several notions of dynamical entropy play a fundamental role, among them: topological entropy and restoration entropy. In this paper, we present a theorem that demonstrates that for most dynamical systems, restoration entropy strictly exceeds topological entropy. This implies that robust estimation policies in general require a higher rate of data transmission than non-robust ones. The proof of our theorem is quite short, but uses sophisticated tools from the theory of smooth dynamical systems.

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