Sage Husa Adaptive Integrated Navigation Algorithm Based on Variable Fading Factor

Huang, Xiangyan; Chen, Guoshao; Liu, Zhiping

doi:10.1109/iccnea50255.2020.00084

2020 International Conference on Computer Network, Electronic and Automation (ICCNEA) 2020

DOI: 10.1109/iccnea50255.2020.00084

|View full text |Cite

Sage Husa Adaptive Integrated Navigation Algorithm Based on Variable Fading Factor

Xiangyan Huang

Guoshao Chen

Zhiping Liu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al ( 24 ) presented an AKF to minimize the measurement error. Recently, Huang et al ( 25 ) have analyzed the changes in noise characteristics in the AKF. However, the AKF can easily lose positive definiteness of the estimation covariance, making the output gradually diverge.…”

mentioning

confidence: 99%

Predicting for Traffic Risk Degree: Novel Prediction Method and Samples

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

To effectively predict the risk degree in both maritime and road traffic, a novel method is proposed in this study. First, the improved Dempster–Shafer evidence theory was derived to address multiple evidence based on the uncertain mass in the traffic environment. Further, the iterative combination equations reduced the computational complexity when computing the traffic risk degree in a given scan. Accordingly, the modified adaptive Kalman filter was explored to predict the traffic risk degree for the next scan. To maintain a positive definiteness in the estimation covariance during the whole filtering process, the Cholesky decomposition was applied to enhance reliability. By transmitting the lower triangular matrix from the Cholesky decomposition of estimation covariance, the computational complexity was reduced relatively. Finally, the experiment results indicated that the proposed method had satisfactory prediction performance for the traffic risk degree.

show abstract

mentioning

confidence: 99%

Predicting for Traffic Risk Degree: Novel Prediction Method and Samples

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

Backward smoothing adaptive SVDCKF integrated navigation algorithm

Lü

Jia

Teng

et al. 2024

Advances in Space Research

View full text Add to dashboard Cite

An Anti-Spoofing Model Based on MVM and MCCM for a Loosely-Coupled GNSS/INS /LiDAR Kalman Filter

Chang,

Zhang,

Fan

et al. 2024

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Sage Husa Adaptive Integrated Navigation Algorithm Based on Variable Fading Factor

Cited by 4 publications

References 4 publications

Predicting for Traffic Risk Degree: Novel Prediction Method and Samples

Predicting for Traffic Risk Degree: Novel Prediction Method and Samples

Backward smoothing adaptive SVDCKF integrated navigation algorithm

An Anti-Spoofing Model Based on MVM and MCCM for a Loosely-Coupled GNSS/INS /LiDAR Kalman Filter

Contact Info

Product

Resources

About