Sigma Point Kalman Filters for Re-entry Ballistic Target Tracking

Authors

Chunling Wu, School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, China;
Yongfeng Ju, School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, China;
Hu Ping, School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, China;
Chengdong Duan, School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, China;

Abstract

Abstract: A new kind of sigma-point Kalman filter was proposed, quadrature Kalman filter(QKF), for the purpose of re-entry ballistic target tracking applications. The new filter linearized the nonlinear functions using statistical linear regression method through a set of Gaussian-Hermite quadrature points that parameterized the Gaussian density. The simulation experiment compared this new sigma point filter with EKF, DDF and UKF. Simulation results show that the estimation errors of all sigma point filters are all lower than that of EKF. The estimation error of QKF is lower than that of UKF, and its filtering credibility is almost same as that of UKF. The calculation complexity of QKF is a litter higher than that of UKF. The new sigma-point filter is an effective algorithm.

Recommended Citation

Wu, Chunling; Ju, Yongfeng; Ping, Hu; and Duan, Chengdong (2020) "Sigma Point Kalman Filters for Re-entry Ballistic Target Tracking," Journal of System Simulation: Vol. 28: Iss. 3, Article 18.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss3/18

First Page

640

Revised Date

2014-12-19

DOI Link

https://doi.org/

Last Page

647

CLC

TP391.9

Recommended Citation

Wu Chunling, Ju Yongfeng, Hu Ping, Duan Chengdong. Sigma Point Kalman Filters for Re-entry Ballistic Target Tracking[J]. Journal of System Simulation, 2016, 28(3): 640-647.