Sigma-Point Kalman Filtering for tightly-coupled GPS/INS

2008 ◽  
Author(s):  
Zhen Guo ◽  
Yanling Hao ◽  
Feng Sun ◽  
Wei Gao
Keyword(s):  
Kalman Filtering ◽  
Sigma Point ◽  
Tightly Coupled

Sigma-Point Kalman Filtering for Tightly Coupled GPS/INS Integration

Navigation ◽  
2008 ◽  
Vol 55 (3) ◽  
pp. 167-177 ◽  
Author(s):  
YONG LI ◽  
CHRIS RIZOS ◽  
JINLING WANG ◽  
PETER MUMFORD ◽  
WEIDONG DING
Keyword(s):  
Kalman Filtering ◽  
Sigma Point ◽  
Tightly Coupled

Precision navigation system design for Moon penetrator

2016 ◽  
Vol 88 (6) ◽  
pp. 791-798
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Yuliang Bai ◽  
Naigang Cui
Keyword(s):  
System Design ◽  
Kalman Filtering ◽  
Navigation System ◽  
Estimation Accuracy ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Navigation Systems ◽  
Vision Sensor ◽  
Content Type ◽  
Sigma Point

Purpose Penetrator plays an important role in the exploration of Moon and Mars. The navigation method is a key technology during the development of penetrator. To meet the high accuracy requirements of Moon penetrator, this paper aims to propose two kinds of navigation systems. Design/methodology/approach The line of sight of vision sensor between the penetrator and Moon orbiter could be utilized as the measurement during the navigation system design. However, the analysis of observability shows that the navigation system cannot estimate the position and velocity of penetrator, when the line of sight measurement is the only resource of information. Therefore, the Doppler measurement due to the relative motion between penetrator and the orbiter is used as the supplement. The other option is the relative range measurement between penetrator and the orbiter. The sigma-point Kalman Filtering is implemented to fuse the information from the vision sensor and Doppler or rangefinder. The observability of two navigation system is analyzed. Findings The sigma-point Kalman filtering could be used based on vision sensor and Doppler radar or laser rangefinder to give an accurate estimation of Moon penetrator position and velocity without increasing the payload of Moon penetrator or decreasing the estimation accuracy. However, the simulation result shows that the last method is better. The observability analysis also proves this conclusion. Practical implications Two navigation systems are proposed, and the simulations show that both systems can provide accurate estimation of states of penetrator. Originality/value Two navigation methods are proposed, and the observability of these navigation systems is analyzed. The sigma-point Kalman filtering is first introduced to the vision-based navigation system for Moon penetrator to provide precision navigation during the descent phase of Moon penetrator.

A covariance shaping filtering method for tightly-coupled MIMU/GNSS of UAV

2019 ◽  
Vol 91 (10) ◽  
pp. 1257-1267 ◽  
Author(s):  
Bin Liu ◽  
Jiangtao Xu ◽  
Bangsheng Fu ◽  
Yong Hao ◽  
Tianyu An
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
Adaptive Filter ◽  
Dynamic Environments ◽  
Content Type ◽  
Filtering Method ◽  
Time Performance ◽  
Dual Channel ◽  
Tightly Coupled ◽  
Parallel Filter

Purpose Regarding the important roles of accuracy and robustness of tightly-coupled micro inertial measurement unit (MIMU)/global navigation satellite system (GNSS) for unmanned aerial vehicle (UAV). This study aims to explore the efficient method to improve the real-time performance of the sensors. Design/methodology/approach A covariance shaping adaptive Kalman filtering method is developed. For optimal performance of multiple gyros and accelerometers, a distribution coefficient of precision is defined and the data fusion least square method is applied with fault detection and identification using the singular value decomposition. A dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed. Findings Hardware-in-the-loop numerical simulation was adopted, the results indicate that the gain of the covariance shaping adaptive filter is self-tuning by changing covariance weighting factor, which is calculated by minimizing the cost function of Frobenius norm. With the improved method, the positioning accuracy with tightly-coupled MIMU/GNSS of the adaptive Kalman filter is increased obviously. Practical implications The method of covariance shaping adaptive Kalman filtering is efficient to improve the accuracy and robustness of tightly-coupled MIMU/GNSS for UAV in complex and dynamic environments and has great value for engineering applications. Originality/value A covariance shaping adaptive Kalman filtering method is presented and a novel dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed, to improve the real-time performance in complex and dynamic environments.

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