Доступ предоставлен для: Guest
Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
International Journal for Uncertainty Quantification
Импакт фактор: 3.259 5-летний Импакт фактор: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN Печать: 2152-5080
ISSN Онлайн: 2152-5099

Свободный доступ

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2016016561
pages 127-140

UNCERTAINTY QUANTIFICATION TOWARDS FILTERING OPTIMIZATION IN SCENE MATCHING AIDED NAVIGATION SYSTEMS

Shengdi Zhang
Department of Mathematics and Systems Science, College of Science, National University of Defense Technology, Changsha, Hunan, HN 731, People's Republic of China
Xiaojun Duan
Department of Mathematics and Systems Science, College of Science, National University of Defense Technology, Changsha, Hunan, HN 731, People's Republic of China
Lijun Peng
Department of Mathematics and Systems Science, College of Science, National University of Defense Technology, Changsha, Hunan, HN 731, People's Republic of China

Краткое описание

There exist many uncertain sources for positioning process of scene matching aided navigation systems, and filter architecture and random parameters are considered mainly in this paper for the goal of trajectory optimization. In order to reduce the uncertainty of filter architecture, a practical scene matching optimization scheme is proposed, where fusion architecture and filtering parameters are both considered, in the circumstance of different sampling rates without transmission delay. The matching interval is determined by adjustment time of matching, matching points are settled from back to front, a cost function for the matching number is designed according to optimization criteria, and finally an appropriate matching number is determined by required mapping accuracy. The optimization scheme is validated by the simulation. On the other hand the filtering parameters are identified and validated by reducing parameter space in advance and local sensitivity analysis method. Simulation results illustrate that the systems are more sensitive for the measurement noise, which provides a theoretical basis for engineering applications.


Articles with similar content:

Minimization of Variance for Multidimensional Processes with Multirate Sampling for Models in Space of States with Delay
Journal of Automation and Information Sciences, Vol.42, 2010, issue 9
Victor D. Romanenko, Alexey A. Reutov
INVESTIGATION INTO THE PARAMETERS OF THE TRAFFIC OF THE GSM NETWORK SECTION UNDER THE EFFECT OF LOCAL OVERFLOWS
Telecommunications and Radio Engineering, Vol.72, 2013, issue 10
I.N. Smetanin, A.G. Lozhkovsky, D. M. Piza, O.V. Verbanov
USE OF HIGH-ORDER STATISTICS IN NON-GAUSSIAN PROCESS RECOGNITION FROM LINEAR PREDICTION MODELS
Telecommunications and Radio Engineering, Vol.74, 2015, issue 5
K. V. Netrebenko, V. A. Tikhonov, I.O. Fil, V. M. Bezruk
A NEW GIBBS SAMPLING BASED BAYESIAN MODEL UPDATING APPROACH USING MODAL DATA FROM MULTIPLE SETUPS
International Journal for Uncertainty Quantification, Vol.5, 2015, issue 4
Sahil Bansal
To the Construction of Parametric Families of Ellipsoidal Estimates and their Optimization in Problems of the Nonstochastic Identification of Parameters and State of Many-Dimensional Discrete Control Objects
Journal of Automation and Information Sciences, Vol.30, 1998, issue 4-5
Victor V. Volosov