Adaptive Kalman filtering based on optimal autoregressive predictive model

Conventional Kalman filter (KF) relies heavily on a priori knowledge of the potentially unstable process and measurement noise statistics. Insufficiently known a priori filter statistics will reduce the precision of the estimated states or introduce biases to the estimates. We propose an adaptive KF...

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Vydáno v:GPS solutions Ročník 21; číslo 2; s. 307 - 317
Hlavní autoři: Jin, Biao, Guo, Jiao, He, Dongjian, Guo, Wenchuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2017
Springer Nature B.V
Témata:
Adaptive algorithms
Adaptive filters
Algorithms
Atmospheric Sciences
Automotive Engineering
Autoregressive models
Autoregressive processes
Computer simulation
Covariance
Earth and Environmental Science
Earth Sciences
Electrical Engineering
Field tests
Geophysics/Geodesy
Innovations
Kalman filters
Noise measurement
Noise prediction
Polynomials
Prediction models
Quadratic programming
Review Article
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
State estimation
Global positioning system (GPS)
Autoregressive (AR) model
Adaptive Kalman filter
Navigation
Quadratic programming
ISSN:1080-5370, 1521-1886
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Shrnutí:Conventional Kalman filter (KF) relies heavily on a priori knowledge of the potentially unstable process and measurement noise statistics. Insufficiently known a priori filter statistics will reduce the precision of the estimated states or introduce biases to the estimates. We propose an adaptive KF based on the autoregressive (AR) predictive model for vehicle navigation. First, the AR model is incorporated into the KF for state estimation. The closed-form solution of the AR model coefficients is obtained by solving a convex quadratic programming problem, which is according to the criterion of minimizing the mean-square error, and subject to the polynomial constraint of vehicle motion. Then, an innovation-based adaptive approach is improved based on the KF with the AR predictive model. In the proposed adaptive algorithm, the process noise covariance is computed using the real-time information of the innovation sequence. Simulation results demonstrate that the KF with the AR model has a higher estimated precision than the KF with the traditional discrete-time differential model under the condition of the same parameter setting. Field tests show that the positioning accuracy of the proposed adaptive algorithm is superior to the conventional adaptive KF.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ObjectType-Literature Review-2
ISSN:1080-5370
1521-1886
DOI:10.1007/s10291-016-0561-x