Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78822
Title: Correlational inference-based adaptive unscented Kalman filter with application in GNSS/IMU-integrated navigation
Authors: Yang, C
Shi, WZ 
Chen, W 
Keywords: Integrated navigation
Unscented Kalman filter
Adaptive estimation
Correlational inference
Issue Date: 2018
Publisher: Springer
Source: GPS solutions, Oct. 2018, v. 22, no. 4, UNSP 100 How to cite?
Journal: GPS solutions 
Abstract: A generalized Kalman filtering estimator with nonlinear models is derived based on correlational inference, in which a new target function with constraint equation is established. Hence, a new unscented Kalman filter (UKF) expression is deduced from this target function. In this new expression, the state estimator is directly related to the predicted states vector, predicted residuals vector, and their covariance matrices as well as their cross-covariance matrix. Furthermore, a new estimator, called adaptive unscented Kalman filter (AUKF), is extended directly from the derived target function to reduce the impact of disturbances of dynamic model and system noise. Simulation and a field test have been conducted to compare the performance of AUKF and conventional UKF, as well as the innovation-based adaptive estimation (IAE) method. The simulation proves that the AUKF outperforms the conventional UKF regarding positioning and velocity estimates. Similarly, the field test also proves the superiority of the AUKF against the conventional UKF. This test also shows that the adaptive factor-based AUKF has similar performance with IAE-based AUKF, but requires less computation time.
URI: http://hdl.handle.net/10397/78822
ISSN: 1080-5370
EISSN: 1521-1886
DOI: 10.1007/s10291-018-0766-2
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Citations as of Mar 16, 2019

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
Citations as of Mar 20, 2019

Page view(s)

8
Citations as of Mar 25, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.