Bayesian estimation for target tracking: part II, the Gaussian sigma-point Kalman filters

2012 ◽  
Vol 4 (5) ◽  
pp. 489-497 ◽  
Author(s):  
A.J. Haug
Keyword(s):  
Target Tracking ◽  
Bayesian Estimation ◽  
Kalman Filters ◽  
Sigma Point

Target Tracking System Using Multiple Cameras and Bayesian Estimation

2016 ◽  
Vol 14 (6) ◽  
pp. 2713-2718
Author(s):  
Diego Gonzalez Dondo ◽  
Javier Andres Redolfi ◽  
Martin Griffa ◽  
Guillermo Max Steiner ◽  
Luis Rafael Canali
Keyword(s):  
Target Tracking ◽  
Bayesian Estimation ◽  
Tracking System ◽  
Multiple Cameras

Infrared small target tracking algorithm based on temporal-spatial structure sparse Bayesian estimation

2020 ◽  
Vol 105 ◽  
pp. 103160 ◽  
Author(s):  
Zhengzhou Li ◽  
Cheng Chen ◽  
Depeng Liu ◽  
Chao Zhang ◽  
Jingjie Zeng ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Target Tracking ◽  
Bayesian Estimation ◽  
Tracking Algorithm ◽  
Small Target ◽  
Infrared Small Target

A comprehensive comparison of sigma-point Kalman filters applied on a complex maneuvering road

2016 ◽  
Author(s):  
Mohammad Al Shabi ◽  
Khaled Hatamleh ◽  
Samer Al Shaer ◽  
Iyad Salameh ◽  
S. Andrew Gadsden
Keyword(s):  
Kalman Filters ◽  
Sigma Point ◽  
Comprehensive Comparison

scholarly journals Erratum to: Joseph covariance formula adaptation to Square-Root Sigma-Point Kalman filters

2017 ◽  
Vol 88 (3) ◽  
pp. 1987-1987 ◽  
Author(s):  
Francesco De Vivo ◽  
Alberto Brandl ◽  
Manuela Battipede ◽  
Piero Gili
Keyword(s):  
Kalman Filters ◽  
Square Root ◽  
Sigma Point

Joseph covariance formula adaptation to Square-Root Sigma-Point Kalman filters

2017 ◽  
Vol 88 (3) ◽  
pp. 1969-1986 ◽  
Author(s):  
Francesco De Vivo ◽  
Alberto Brandl ◽  
Manuela Battipede ◽  
Piero Gili
Keyword(s):  
Kalman Filters ◽  
Square Root ◽  
Sigma Point

scholarly journals A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

2021 ◽  
Author(s):  
Bataa Lkhagvasuren ◽  
Minkyu Kwak ◽  
Hong Sung Jin ◽  
Gyuwon Seo ◽  
Sungyool Bong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Real Data ◽  
Estimation Algorithm ◽  
Open Circuit ◽  
State Of Charge ◽  
Least Square ◽  
State Equations ◽  
Sigma Point ◽  
Parameter Dependent

<div>This paper proposes a new window-wise state of charge (SOC) estimation algorithm based on Kalman filters (KF). In the first stage, the equivalent circuit model's parameters are estimated by a least square estimation window-wise, assuming a linear SOC and open-circuit voltage (OCV) relation. The algorithm accurately estimates the parameters and observes the changes that depend on SOC. Moreover, based on the estimated parameters, the OCV values are identified. In the next stage, window-wise linear Kalman filter(ES-LKF) without hysteresis and extended Kalman filter (ES-EKF) and sigma-point Kalman filter (ES-SPKF) algorithm with hysteresis are executed to estimate SOC. Having fewer state equations and hysteresis parameters tuned up in an off-line way, the ES-EKF and ES-SPKF perform better than the algorithms considered in previous works. The algorithms are validated by experiments with real data obtained from lab tests.</div>

Sign in / Sign up

Export Citation Format

Share Document