On-line optimal design of process noise covariance in nonlinear Kalman Filters: A hemodynamic model application

2016 ◽  
Author(s):  
Mahmoud K. Madi ◽  
Fadi N. Karameh
Keyword(s):  
Optimal Design ◽  
Kalman Filters ◽  
Process Noise ◽  
Model Application ◽  
Hemodynamic Model ◽  
Noise Covariance ◽  
On Line

scholarly journals On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters

2019 ◽  
Vol 33 ◽  
pp. 4098-4105 ◽  
Author(s):  
Mark Kozdoba ◽  
Jakub Marecek ◽  
Tigran Tchrakian ◽  
Shie Mannor
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Linear Dynamical System ◽  
Process Noise ◽  
Linear Dynamical Systems ◽  
The Past ◽  
On Line ◽  
Regression Depth ◽  
Regret Bounds ◽  
On Line Learning

The Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever the process noise is non-degenerate. Therefore, Kalman filter may be approximated by regression on a few recent observations. Surprisingly, we also show that having some process noise is essential for the exponential decay. With no process noise, it may happen that the forecast depends on all of the past uniformly, which makes forecasting more difficult.Based on this insight, we devise an on-line algorithm for improper learning of a linear dynamical system (LDS), which considers only a few most recent observations. We use our decay results to provide the first regret bounds w.r.t. to Kalman filters within learning an LDS. That is, we compare the results of our algorithm to the best, in hindsight, Kalman filter for a given signal. Also, the algorithm is practical: its per-update run-time is linear in the regression depth.

scholarly journals Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1126
Author(s):  
Zhentao Hu ◽  
Linlin Yang ◽  
Yong Jin ◽  
Han Wang ◽  
Shibo Yang
Keyword(s):  
Real Time ◽  
Gaussian Mixture ◽  
Measurement Noise ◽  
Tracking Accuracy ◽  
Process Noise ◽  
Probability Hypothesis Density ◽  
Measurement Noise Covariance ◽  
Multi Target Tracking ◽  
Noise Covariance ◽  
Fading Factor

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.

Online evaluation of the process noise covariance matrix for event‐based state estimators

2019 ◽  
Vol 120 (2) ◽  
pp. 195-208
Author(s):  
Miguel Martínez‐Rey ◽  
Carlos Santos ◽  
Rubén Nieto ◽  
Cristina Losada ◽  
Felipe Espinosa
Keyword(s):  
Covariance Matrix ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Online Evaluation ◽  
State Estimators ◽  
Noise Covariance ◽  
Event Based

Research and Implementation of the Optimal Design Pattern of Electronic Commerce Based on J2EE

2011 ◽  
Vol 143-144 ◽  
pp. 849-853
Author(s):  
Yi Ran Wang ◽  
Xing Jin Zhang
Keyword(s):  
Optimal Design ◽  
Electronic Commerce ◽  
Design Pattern ◽  
System Development ◽  
System Structure ◽  
Application Layer ◽  
Development Cycle ◽  
On Line ◽  
The Cost ◽  
J2ee Architecture

Analyzed and recomposed to J2EE architecture, and during research of design pattern, the document bases on existing frame for achieving more application-layer reuse to engineer framework, and forms an optimal design pattern of electronic commerce. The pattern is actualized in on-line shopping system found on J2EE. The experiment results show that it reduces system development cycle, optimizes system structure, enhances the system's performance, and decreases the risk and the cost in software development when the framework and design pattern are combined artfully to use on electronic commerce

scholarly journals A System for Tracking an Autonomously Controlled Canine

2012 ◽  
Vol 65 (3) ◽  
pp. 427-444 ◽  
Author(s):  
Jeff Miller ◽  
George Flowers ◽  
David Bevly
Keyword(s):  
Fuzzy Logic Controller ◽  
Gps Receiver ◽  
Process Noise ◽  
Embedded Control ◽  
Noise Covariance Matrix ◽  
Adaptive Tuning ◽  
Noise Characteristics ◽  
Noise Covariance ◽  
Motion Characteristics ◽  
Tone Generation

This paper presents an approach for outdoor navigation of an autonomously guided canine using an embedded command module with vibration and tone generation capabilities and an embedded control suite comprised of a microprocessor, wireless radio, GPS receiver, and an Attitude and Heading Reference System. In order to determine the canine's motions, which inherently contain non-conventional noise characteristics, the sensor measurements were integrated using a specialized Extended Kalman Filter (EKF), equipped with a Fuzzy Logic controller for adaptive tuning of the Process Noise Covariance Matrix. This allowed for rejection of un-modelled canine motion characteristics which tend to corrupt accelerometer bias tracking in a standard EKF. The EKF solution provided an optimized estimate of the canine position and velocity and also proved to be effective in tracking the canine's position (within 7·5 m) and velocity (within 1·2 m/s) during simulated 10 second GPS outages.

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