Comparison of particle filtering and extended Kalman filtering for acoustic-based tracking of low-flying aircraft

2011 ◽  
Vol 130 (4) ◽  
pp. 2329-2329
Author(s):  
Wm. Garth Frazier ◽  
Chad Williams
Keyword(s):  
Kalman Filtering ◽  
Particle Filtering ◽  
Extended Kalman Filtering

scholarly journals An OGI model for personalized estimation of glucose and insulin concentration in plasma

2021 ◽  
Vol 18 (6) ◽  
pp. 8499-8523
Author(s):  
Weijie Wang ◽  
◽  
Shaoping Wang ◽  
Yixuan Geng ◽  
Yajing Qiao ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Gaussian Noise ◽  
Particle Filtering ◽  
In Silico ◽  
Insulin Concentration ◽  
Model Parameters ◽  
Bayesian Filtering ◽  
Extended Kalman Filtering ◽  
External Disturbances ◽  
Non Gaussian

<abstract><p>Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC $ 9.49\pm3.81 $ mU/L, and PGC $ 0.89\pm0.19 $ mmol/L. For human, the OGI with PFM has the promise to identify disturbances ($ 95.46\%\pm0.65\% $ accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.</p></abstract>

scholarly journals COMPARISON OF GPS SMOOTHENING METHODS BETWEEN EXTENDED KALMAN FILTER AND PARTICLE FILTER FOR UAV

2017 ◽  
Keyword(s):  
Numerical Simulation ◽  
Kalman Filter ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
State Vector ◽  
The State ◽  
Gps Data ◽  
Extended Kalman Filtering

This paper presents a method for smoothing GPS data from a UAV using Extended Kalman filtering and particle filtering for navigation or position control. A key requirement for navigation and control of any autonomous flying or moving robot is availability of a robust attitude estimate. Consider a dynamic system such as a moving robot. The unknown parameters, e.g., the coordinates and the velocity, form the state vector. This time dependent vector may be predicted for any instant time by means of system equations. The predicted values can be improved or updated by observations containing information on some components of the state vector. The whole procedure is known as Kalman filtering. On the other hand, the particle filtering algorithm is to perform a recursive Bayesian filter by Monte Carlo simulations. The key is to represent the required posterior density function by a set of random samples, which is called particles with associated weights, and to compute estimates based on these samples as well as weights. We compare the two GPS smoothening methods: Extended Kalman Filter and Particle Filter for mobile robots applications. Validity of the smoothing methods is verified from the numerical simulation and the experiments. The numerical simulation and experimental results show the good GPS data smoothing performance using Extended Kalman filtering and particle filtering.

A Survey on Moving Target Tracking in the Intelligent Visual Monitoring System

2014 ◽  
Vol 599-601 ◽  
pp. 790-793 ◽  
Author(s):  
Meng Xin Li ◽  
Gao Ling Su ◽  
Jing Hou ◽  
Dai Zheng
Keyword(s):  
Target Tracking ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
Visual Surveillance ◽  
Moving Target ◽  
Extended Kalman Filtering ◽  
Moving Target Tracking ◽  
Filtering Algorithm ◽  
Tracking Algorithms ◽  
Kalman Filtering Algorithm

Moving target tracking is the key part of intelligent visual surveillance system. Among the various tracking algorithms, the Beysian tracking algorithms and the kernel tracking algorithm are two algorithms that frequently used. The Beysian tracking algorithms mainly conclude Kalman filtering algorithm, extended Kalman filtering algorithm and particle filtering algorithm. Mean Shift is the most representative algorithm of the kernel target tracking. In this survey, the status and development of target tracking algorithms has been studied more extensively with providing a few examples of modified tracking algorithms. Then a comparison was presented based on the limitations and scope of applications. Finally, the paper showed further research prospects of moving target tracking are introduced.

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