WSEAS Transactions on Mathematics


Print ISSN: 1109-2769
E-ISSN: 2224-2880

Volume 16, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.


Volume 16, 2017



Time Registration Algorithm of Multi Sensors Based on Spline Interpolating

AUTHORS: Zhimin Chen, Yuming Bo, Wenhua Zhao, Liang Pan, Jiahong Chen

Download as PDF

ABSTRACT: The time registration algorithm applicable to modern high-accuracy target tracking system is designed based on cubic spline interpolation. This algorithm can perform time registration for any quantity of sensors, the sensor measurement short of data, and the measurement of the sensor with non-uniform sampling period. The sampling rates obtained through registration can be selected according to system requirements to provide effective time registration methods for modern high-accuracy target tracking system.

KEYWORDS: Spline interpolating, multi sensors, data fusion, time registration, target tracking, sampling rates

REFERENCES:

[1] Z. Zhang and G. Shan, Sensor Scheduling for Target Tracking Using Approximate Dynamic Programming, Wseas Transactions on Systems & Control, vol.8, no.4, 2013, pp. 121-130.

[2] A. Morrison, V. Renaudin, J. B. Bancrof, et al., Design and testing of a multi-sensor pedestrian location and navigation platform, Sensors, vol.12, no.3, 2012, pp. 3720-3738.

[3] A. A. Fathima, S. Vasuhi, T. M. Treesa, et al., Person Authentication System with Quality Analysis of Multimodal Biometrics, WSEAS Transactions on Information Science & Applications, vol.10, no.6, 2013, pp. 179-194.

[4] J. Q. Lu, P. Wei, Z. Chen, A Scheme to Counter SSDF Attacks based on Hard Decision in Cognitive Radio Networks, WSEAS Transactions on Communications, vol.13, 2014, pp. 242-248.

[5] J. A. Rodger, Toward reducing failure risk in an integrated vehicle health maintenance system: A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS, Expert Systems with Applications, vol.39, no.10, 2012, pp. 9821-9836.

[6] J. Jiao, Z. Deng, B. Zhao, et al, A Hybrid Method for Multi-sensor Remote Sensing Image Registration Based on Salience Region, Circuits, Systems, and Signal Processing, vol.33, no.7, 2014, pp. 2293-2317.

[7] S. Abdikan, F. Balik Sanli, F. Sunar, et al., A comparative data-fusion analysis of multi-sensor satellite images, International Journal of Digital Earth, vol.7, no.8, 2014, pp. 671-687.

[8] T. Schenk, B. Csatho, C. van der Veen, et al., Fusion of multi-sensor surface elevation data for improved characterization of rapidly changing outlet glaciers in Greenland, Remote Sensing of Environment, vol.149, 2014, pp. 239-251.

[9] L. Hegarat, S. Bloch S, Application of dumpsterShafer evidence theory to unsupervised classi- fication in multisource remote sensing, IEEE Transactions on Geoscience and Remote Sensing, vol.35, no.4, 1997, pp. 1018-1031.

[10] M. Datcu, F. Melgani, A multisource data classification with dependence trees, IEEE Transactions on Geoscience and Remote Sensing, vol.40, no.3, 2002, pp. 609-617.

[11] F. Franceschini, M. Galetto, D. Maisano, et al., Large-scale dimensional metrology (LSDM): from tapes and theodolites to multi-sensor systems, International Journal of Precision Engineering and Manufacturing, vol.15, no.8, 2014, pp. 1739-1758.

[12] H. Karniely, T. H. Siegelmann, Sensor registration using neural networks, IEEE Transactions on Aerospace and Electronic Systems, vol.35, no.1, 2000, pp. 85-101.

[13] X. Lin, Y. Bar-shalom, T. Kirubarajan, Multisensor-multitarget bias estimation for general asynchronous sensors, IEEE transactions on aerospace and electronic systems, vol.41, no.1, 2005, pp. 899-921.

[14] Y. Jin, Y. Ding, K. Hao, et al., An endocrinebased intelligent distributed cooperative algorithm for target tracking in wireless sensor networks. Soft Computing, vol.19, no.5, 2015, pp. 1427-1441.

[15] Y. E. M. Hamouda, C. Phillips, Adaptive sampling for energy-efficient collaborative multitarget tracking in wireless sensor networks, IET wireless sensor systems, vol.1, no.1, 2011, pp. 15-25.

[16] D. Janczak, M. Sankowski, Data fusion for ballistic targets tracking using least squares, International Journal of Electronics and Communications, vol.66, no.6, 2012, pp. 512-519.

[17] N. Nabaa and R. H. Bishop, Solution to a Multisensor tracking problem with sensor registration errors,IEEE Transactions on Aerospace and Electronic Systems, vol.35, no.1, 1999, pp. 354- 363.

[18] W. Tian, Y. Wang, X. Shan, et al., Track-to-track association for biased data based on the reference topology feature. IEEE Signal Processing Letters, vol.21, no.4, 2014, pp. 449-453.

[19] W. Li, H. Leung and Y. Zhou, Space-Time Registration of Radar and ESM Using Unscented Kalman Filter, IEEE Transaction on Aerospace and Electronic Systems, vol.40, no.3, 2004, pp. 824-836.

[20] Y. Fu, Q. Ling and Z. Tian, Distributed sensor allocation for multi-target tracking in wireless sensor networks, IEEE Transaction on Aerospace and Electronic Systems, vol.48, no.4, 2012, pp. 3538-3553.

WSEAS Transactions on Mathematics, ISSN / E-ISSN: 1109-2769 / 2224-2880, Volume 16, 2017, Art. #17, pp. 143-151


Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

Bulletin Board

Currently:

The editorial board is accepting papers.


WSEAS Main Site