scholarly journals Unscented Kalman Filter for Unobservable Parameter Estimation in Heart Cell Signals

1970 ◽  
Vol 4 ◽  
pp. 27-28
Author(s):  
David Adolfo Sampedro-Puente ◽  
Jesús Fernández-Bes ◽  
Esther Pueyo
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Cell Membrane ◽  
Kalman Filters ◽  
Unscented Kalman Filter ◽  
Heart Cell ◽  
Channel Dynamics ◽  
Proarrhythmic Risk ◽  
Organ Level ◽  
Unobservable Parameter

One interesting feature of biological systems is that minor subcellular changes can cause alterations at the whole organ level. In the heart, the random dynamics of cell membrane ion channels contributes to beat-to-beat repolarization variability, which has been related to proarrhythmic risk. Inference of unobservable cellular parameters, such as the number of channels, is key to characterize such random ion channel dynamics. In this work, a methodology based on the use of Unscented Kalman Filters is proposed to infer the number of channel from action potential signals, like those commonly recorded experimentally.

scholarly journals Particle Swarm Optimization Based Adaptive Kalman Filters for Fault Diagnosis of Reaction Wheels

2021 ◽  
Author(s):  
Afshin Rahimi
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Unscented Kalman Filter ◽  
Particle Swarm ◽  
Filter Parameter ◽  
Filter Performance ◽  
Filter Tuning ◽  
Selection Of

There has been an increasing interest in fault diagnosis in recent years, as a result of the growing demand for higher performance, efficiency, reliability and safety in control systems. A faulty sensor or actuator may cause process performance degradation, process shut down, or a fatal accident. Quick fault detection and isolation can help avoid abnormal event progression and minimize the quality and productivity offsets. In space systems specifically, space and power are limited in the satellites, which means that hardware redundancy is not very practical. If actuator faults occur, analytical redundancy techniques should be employed to determine if, where, and how the fault(s) occurred. To do so, different approaches have been developed and studied and one of the wellknown approaches in the literature is using the Kalman Filter as an observer for the purpose of parameter estimation and fault detection. The gains for the filter should be selected and the selection of the process and measurement noise statistics, commonly referred to as “filter tuning,” is a major implementation issue for the Kalman filter. This process can have a significant impact on the filter performance. In practice, Kalman filter tuning is often an ad-hoc process involving a considerable amount of time for trial and error to obtain a filter with desirable –qualitative or quantitative- performance characteristics. This thesis focuses on presenting an algorithm for automation of the selection of the gains using an evolutionary swarm intelligence based optimization algorithm (Particle Swarm) to minimize the residuals of the estimated parameters. The methodology can be applied to any filter or controller but in this thesis, an Adaptive Unscented Kalman Filter parameter estimation applied to a reaction wheel unit is used for the purpose of performance evaluation of the proposed methodology.

Drag Coefficient Identification from Flight Data via Optimal Observer

2014 ◽  
Vol 687-691 ◽  
pp. 787-790
Author(s):  
Rong Jun Yang ◽  
Yao Ye
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Drag Coefficient ◽  
Dynamic Equation ◽  
Unscented Kalman Filter ◽  
Flight Test ◽  
Measurement Noise ◽  
Estimation Technique ◽  
Flight Data ◽  
Coefficient Identification

. For effectively using flight test data to extract drag coefficient, an optimal observer based on parameter estimation technique is proposed. The point mass dynamic equation is used to form the Unscented Kalman Filter (UKF) and the smoother (URTSS) for the estimation of a projectile’s flight states. The projectile flight states are then solved and utilized to extract the drag coefficient information using the observer techniques. The simulation verifies the feasibility of the method: with measurement noise, the accurate drag coefficient is obtained by using the smoother.

Parameter Estimation of 2-DOF System Based on Unscented Kalman Filter

2012 ◽  
Vol 29 (10) ◽  
pp. 1128-1136 ◽  
Author(s):  
Ji-Hoon Seung ◽  
Tae-Yeong Kim ◽  
Amir Atiya ◽  
Alexander Parlos ◽  
Kil-To Chong
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Unscented Kalman Filter

On-Line Performance Monitoring and Engine Diagnostic Using Robust Kalman Filtering Techniques

2003 ◽  
Author(s):  
Pierre Dewallef ◽  
Olivier Le´onard
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Kalman Filters ◽  
Performance Monitoring ◽  
Stochastic Methods ◽  
Inlet Temperature ◽  
State Variables ◽  
Present Contribution ◽  
Regression Problem ◽  
On Line

In this contribution, an on-line engine performance monitoring is carried out through an engine health parameter estimation based on several gas path measurements. This health parameter estimation makes use of the analytical redundancy of an engine model and therefore implies the knowledge of the engine state. As the latter is a priori not known the second task is therefore an engine state variable estimation. State variables here designate working conditions such as inlet temperature, pressure, Mach number, rotational speeds, … Estimation of the state variables constitutes a general application of the Extended Kalman Filter theory, while the health parameter estimation is a classical recurrent regression problem. Recent advances in stochastic methods [1] show that both problems can be solved by two Kalman filters working jointly. Such filters are usually named Dual Kalman Filters. The present contribution aims at using a dual Kalman filter modified to provide robustness. This procedure should be able to cope with as much as 20 to 30% of faulty data. The resulting online method is applied to a turbofan model developed in the frame of the OBIDICOTE 1 project. Several tests are carried out to check the performance monitoring capability and the robustness that can be achieved.

Sign in / Sign up

Export Citation Format

Share Document