A Novel Infrared Landmark Indoor Positioning Method Based on Improved IMM-UKF

Yi Zhang; Hong Song Chen; Yuan Luo

doi:10.4028/www.scientific.net/AMM.511-512.880

Paper Titles

Dynamic Deployment of Wireless Sensor Networks by an Improved Artificial Bee Colony Algorithm
p.862

Application of Supervisory Predictive Control Based on T-S Model in pH Neutralization Process
p.867

Research on Classification Effectiveness of the Novel Mamdani Fuzzy Classifier
p.871

Delay-Dependent H_∞ Filtering for Neural Networks with Time Delay
p.875

A Novel Infrared Landmark Indoor Positioning Method Based on Improved IMM-UKF
p.880

A Two-Stage Optimization Algorithm of Logistics Transportation Network
p.886

Algorithms of Admission Control and Batch Scheduling of On-Demand Broadcast with Deadlines
p.892

An Improved PD Type Iterative Learning Control Algorithm Based on Wavelet Filter
p.898

Weighted K-Means Clustering Analysis Based on Improved Genetic Algorithm
p.904

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 511-512A Novel Infrared Landmark Indoor Positioning...

A Novel Infrared Landmark Indoor Positioning Method Based on Improved IMM-UKF

Abstract:

In structured environment, according to the requirement of indoor robot navigation for accuracy and real-time performance, On the basis of a novel positioning method using infrared landmarks, another novel infrared landmark indoor positioning method which uses high power infrared tube as landmarks, infrared camera as receiving sensor ,and combines track deduction is proposed in this paper. An improved Interacting Multiple Models Unscented Kalman Filter (IMM-UKF) data fusion algorithm for the two positioning scheme is used to improve the precision. Experimental results show that the novel infrared landmark indoor positioning method can increase the location speed and precision effectively.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 511-512)

Pages:

880-885

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.511-512.880

Citation:

Cite this paper

Online since:

February 2014

Authors:

Yi Zhang, Hong Song Chen*, Yuan Luo

Keywords:

IMM-UKF, Indoor Positioning, Infrared Landmark, Track Deduction

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Javier Civera, Oscar G. Grasa, Andrew J. Davison, J. M. M. Montiel. 1-Point RANSAC for EKF-Based Structure from Motion. In IEEE Intelligent Robots and Systems, 2009: 3498-3504.

DOI: 10.1109/iros.2009.5354410

Google Scholar

[2] Jun Cai, Shuanglong Zhang, Yi Zhang, Design and Implementation of Positioning System Based On Smart Space for Intelligent Wheelchair[C]. Applied Mechanics and Materials, 2012, 8: 220-223.

DOI: 10.4028/www.scientific.net/amm.220-223.929

Google Scholar

[3] BAI Yun, YU Li, XIE Changsheng, ZHANG Donghua. A Model-independent Fast Localization Algorithm of WiFi Access Point[J]. Journal of Chinese Computer Systems. 2013, 34(2): 220-223.

Google Scholar

[4] Yu Shuping, Li Wenfeng. A vehicle trajectory deduction algorithm in vehicular ad hoc networking[J]. Computer Applications and Software. 2012, 12：264-266.

Google Scholar

[5] Li Chenglong, Hu Peng, Luo Yangyu, mobile robot positioning system and method based on Infrared landmark. China，CN102419178A[P]. 2012-04-18.

Google Scholar

[6] Chong Yang, Zhang Ke, Lu Meibai. Exploring a Better IMM-UKF Fusion Algorithm Based on Current Statistical Model in Target Tracking[J]. Journal of Northwestern Polytechnical University. 2011, 29(6)：919-926.

Google Scholar

[7] ZHAO Lin, WANG Xiao-Xu, SUN Ming, DING Ji-Cheng, YAN Chao. Adaptive UKF Filtering Algorithm Based on Maximum a Posterior Estimation and Exponential Weighting[J]. ACTA AUTOMATICA SINICA. 2010, 7: 1007-1019.

DOI: 10.3724/sp.j.1004.2010.01007

Google Scholar

[8] Bradski,G. learn opencv[M]. Tsinghua University press. 2009, 10.

Google Scholar