Robust Tracking of Nonlinear Target Motion Using Out-of-Order Sigma Point Kalman Filters

2012 ◽  
Author(s):  
Hyukseong Kwon ◽  
Daniel Pack
Keyword(s):  
Kalman Filters ◽  
Target Motion ◽  
Robust Tracking ◽  
Sigma Point

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>

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>

INFLUENCE OF A PRIORI INFORMATION ON THE MULTIPLE MODEL ALGORITHM CONVERGENCE DURING TRACKING A BALLISTIC TARGET

2020 ◽  
Author(s):  
Д.С. Голенко ◽  
М.И. Сычев
Keyword(s):  
Mathematical Modeling ◽  
Kalman Filters ◽  
A Priori ◽  
Multiple Model ◽  
A Priori Information ◽  
Extended Kalman Filters ◽  
Sigma Point ◽  
Passive Antenna ◽  
Priori Information ◽  
Model Algorithm

Рассмотрена задача сопровождения маневрирующего баллистического объекта на этапе входа в атмосферу с помощью пассивной антенной решетки. Предложено использовать многомодельный алгоритм на основе расширенного и сигма-точечного фильтров Калмана. Проанализировано влияние точности априорной информации на сходимость многомодельного алгоритма. С помощью математического моделирования проведено сравнение с одиночными фильтрами Калмана. The problem of reentry ballistic target tracking with a passive antenna array is considered. Multiple model algorithm based on extended and sigma-point Kalman filters is proposed. A priori information accuracy influence on the convergence of multiple model algorithm is analyzed. Using mathematical modeling, the results were compared with regular extended Kalman filters.

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