TY - JOUR
T1 - Sage windowing and random weighting adaptive filtering method for kinematic model error
AU - Gao, Shesheng
AU - Wei, Wenhui
AU - Zhong, Yongmin
AU - Subic, Aleksandar
PY - 2015/6/22
Y1 - 2015/6/22
N2 - This paper presents a new method for adaptive estimation of kinematic model error in dynamic aircraft navigation. This method combines the concepts of random weighting and Sage windowing to online monitor predicted and observation residuals to control the influence of the kinematic model's systematic error on system state estimation. Based on the Sage windowing, random weighting estimations are constructed within a moving time window for the systematic error of the kinematic model as well as the covariance matrices of the observation noise vector, the predicted residual vector, and the predicted state vector. Experimental results and comparison analysis demonstrate that the proposed method not only adjusts the covariance matrices of the observation noise vector and the predicted residual vector, but also effectively controls the influence of the kinematic model error on state parameter estimation, thus improving the navigation accuracy.
AB - This paper presents a new method for adaptive estimation of kinematic model error in dynamic aircraft navigation. This method combines the concepts of random weighting and Sage windowing to online monitor predicted and observation residuals to control the influence of the kinematic model's systematic error on system state estimation. Based on the Sage windowing, random weighting estimations are constructed within a moving time window for the systematic error of the kinematic model as well as the covariance matrices of the observation noise vector, the predicted residual vector, and the predicted state vector. Experimental results and comparison analysis demonstrate that the proposed method not only adjusts the covariance matrices of the observation noise vector and the predicted residual vector, but also effectively controls the influence of the kinematic model error on state parameter estimation, thus improving the navigation accuracy.
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-84934342607&doi=10.1109%2fTAES.2015.130656&origin=inward&txGid=326b3ca2fba10ea6a52c296ec402ca46
UR - https://ieeexplore.ieee.org/document/7126198
U2 - 10.1109/TAES.2015.130656
DO - 10.1109/TAES.2015.130656
M3 - Article
SN - 0018-9251
VL - 51
SP - 1488
EP - 1500
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
IS - 2
ER -