Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology


An Improved Data Correlation Algorithm for Multi-passive-sensor Tracking System

Lijing Zhang, Yuxiao Song and Li Zhou
School of Information and Electrical Engineering, Ludong University, Yantai 264025, China
Research Journal of Applied Sciences, Engineering and Technology  2014  10:2106-2111
http://dx.doi.org/10.19026/rjaset.7.504  |  © The Author(s) 2014
Received: July 4, 2013  |  Accepted: July 23, 2013  |  Published: March 15, 2014

Abstract

For improving the performance of the optimal assignment problem of data correlation of multi-passive-sensor system, an improved optimal assignment algorithm based on multi-source information fusion is put forward. The new algorithm takes advantage of the optimal solution and a certain number of near-optimal solutions of the traditional optimal assignment problem to construct a set of effective multi-tuple of measurement and constructs correlation probability fusing multi-source information between above effective multi-tuple of measurement and target track by using combination rule of D-S evidence theory. The result of simulation experiments shows that, compared with the traditional optimal assignment algorithm, the new algorithm not only improves the accuracy of multi-target tracking in different degrees but also saves a lot of time. So it is an effective data correlation algorithm for multi-passive-sensor system.

Keywords:

Combination rule of D-S evidence theory, data correlation, multi-tuple of measurement, the optimal assignment algorithm,


References

  1. Chummun, M.R., T. Kirubarajar, K.R. Pattipati and Y. Bar-Shalom, 2001. Fast data association using multidimensional assignment with clusering. IEEE T. Aero. Elec. Sys., 37(3): 898-913.
    CrossRef    
  2. Deb, S., M. Yeddanapudi and K.R. Pattipati, 1997. A generalized S-D assignment algorithm for multisensor-multitarget state estimation. IEEE T. Aero. Elec. Sys., 33(2): 523-537.
    CrossRef    
  3. Goiri, I., 2010. Energy-aware scheduling in virtualized datacenters. Proceedings of the 12th IEEE International Conference on Cluster Computing, pp: 58-67.
    CrossRef    
  4. Han, C.Z., H.Y. Zhu and Z.S. Duan, 2010. Multi-Source Information Fusion. Tsinghua University Press, Beijing.
  5. He, Y., G.H. Wang, D.J. Lu and Y.N. Peng, 2010. Multisensor Information Fusion with Applications. Publishing House of Electronics Industry, Beijing.
  6. Pattipati, K.R., S. Deb, Y. Bar-Shalom and R.B. Washburn, 1992. A new relaxation algorithm and passive sensor data association. IEEE T. Automat. Contr., 37(2): 198-213.
    CrossRef    
  7. Popp, R., K. Pattipati and Y. Bar-Shalom, 2001. M-best S-D assignment algorithm with application to multitarget tacking. IEEE T. Aero. Elec. Sys., 37(1): 22-39.
    CrossRef    
  8. Wang, J.G., J.Q. Luo and X.M. Jin, 2006. Probabilistic data association algorithm of passive tracking based on information fusion with gray correlation analysis. Acta Electron. Sinica, 34(3): 391-395.

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved