Journal ArticleDOI
A new direct filtering approach to INS/GNSS integration
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TLDR
A refined strong tracking unscented Kalman filter (RSTUKF) is developed to enhance the UKF robustness against kinematic model error and maintains the optimal UKF estimation in the absence of kinematics model error.About:
This article is published in Aerospace Science and Technology.The article was published on 2018-06-01. It has received 121 citations till now. The article focuses on the topics: Inertial navigation system & GNSS applications.read more
Citations
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Journal ArticleDOI
Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system
TL;DR: A new adaptive UKF with process noise covariance estimation is proposed to enhance the UKF robustness against process noise uncertainty for vehicular INS/GPS integration.
Book ChapterDOI
INS/GNSS Integrated Navigation Technology
Xuefeng Li,Chaobing Li +1 more
TL;DR: As one type of autonomous navigation system, the inertial navigation does not need to receive any external information and could solve the motional parameters of position, velocity, and attitude via navigation computer, which is the most important navigation system for the OTV.
Journal ArticleDOI
Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration
TL;DR: The MP-UKF could predict the dynamic model error persistently and correct the filtering procedure of UKF online, it improves the UKF adaptiveness and is promising for the performance enhancement of INS/GNSS integration for hypersonic vehicle navigation.
Journal ArticleDOI
Cubature rule-based distributed optimal fusion with identification and prediction of kinematic model error for integrated UAV navigation
TL;DR: Simulations and experimental results as well as comparison analysis demonstrate that the proposed distributed optimal fusion method can effectively identify and predict kinematic model error and further achieve globally optimal fusion results, leading to improved performance for integrated MIMU/GNSS/CNS UAV navigation.
Journal ArticleDOI
Study on Installation Error Analysis and Calibration of Acoustic Transceiver Array Based on SINS/USBL Integrated System
TL;DR: A dynamic calibration algorithm of installation error angle based on incremental iteration is proposed in this paper, which could realize the dynamic on-line estimation of Installation error angle of USBL transceiver array.
References
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Book
Statistical Methods for Research Workers
TL;DR: The prime object of as discussed by the authors is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.
Journal ArticleDOI
A new method for the nonlinear transformation of means and covariances in filters and estimators
TL;DR: A new approach for generalizing the Kalman filter to nonlinear systems is described, which yields a filter that is more accurate than an extendedKalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter.
Book
Principles of GNSS, Inertial, and Multi-Sensor Integrated Navigation Systems
TL;DR: In this paper, the authors present a single-source reference for navigation systems engineering, providing both an introduction to overall systems operation and an in-depth treatment of architecture, design, and component integration.
Journal ArticleDOI
Performance evaluation of UKF-based nonlinear filtering
K. Xiong,H. Y. Zhang,C.W. Chan +2 more
TL;DR: It is proved that under certain conditions, the estimation error of the modified unscented Kalman filter remains bounded, and the Cramer-Rao lower bound (CRLB) is introduced as a performance measure.
Journal ArticleDOI
Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications
TL;DR: A two-tier approach is proposed for improving the stochastic modeling of MEMS-based inertial sensor errors using autoregressive processes at the raw measurement level and enhancing the positioning accuracy during GPS outages by nonlinear modeling of INS position errors at the information fusion level using neuro-fuzzy modules, which are augmented in the Kalman filtering INS/GPS integration.