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Joint Kinematic and Feature Tracking of Ships with Satellite Electronic Information

Published online by Cambridge University Press:  30 April 2018

Yong Liu ,
Libo Yao ,
Wei Xiong  and
Zhimin Zhou
Yong Liu*
Affiliation:
(School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China)
Libo Yao
Affiliation:
(Institute of Information Fusion, Naval Aeronautical and Astronautically University, Yantai, China)
Wei Xiong
Affiliation:
(Institute of Information Fusion, Naval Aeronautical and Astronautically University, Yantai, China)
Zhimin Zhou
Affiliation:
(School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China)
*
(E-mail: [email protected])
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Abstract

To track multiple ships and estimate the feature parameters of multiple emitters on board using electronic intelligence satellites under clutter interference, a long and random revisit time, and other complex conditions, a novel tracking algorithm using both kinematic (position and velocity) and feature information based on an improved Multiple Hypothesis Tracking (MHT) approach is proposed in this paper. Firstly, the characteristics of multi-ship tracking with multiple emitters using satellite electronic information are analysed, and a new model of an emitter is built as an extended target in geographical coordinates. Secondly, a pre-processing of measurements is utilised via hierarchical clustering using the location and feature information of emitters. Thirdly, feature information is incorporated into the MHT framework using Jensen-Shannon divergence distance and fuzzy C-means clustering to calculate track scores. Finally, we present the prediction and update of target states, especially the update of feature parameters, to realise joint kinematic and feature tracking of ships. The results of the simulation show that the proposed method has much better tracking performance than the standard MHT algorithm.

Keywords

Satellite electronic informationMultiple hypothesis trackingExtended target trackingClustering
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 5 , September 2018 , pp. 1178 - 1194
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Baum, M. and Hanebeck, U.D. (2014). Extended Object Tracking with Random Hypersurface Models. IEEE Transactions on Aerospace and Electronic Systems, 50, 149–159.Google Scholar
Blackman, S. and Popoli, R. (1999). Design and Analysis of Modern Tracking Systems. Norwood, MA: Artech House.Google Scholar
Blackman, S.S. (2004). Multiple Hypothesis Tracking for Multiple Target Tracking. Aerospace & Electronic Systems Magazine IEEE, 19, 5–18.Google Scholar
Bowditch, N. (2011). The American Practical Navigator. Wallabycreek Pub.Google Scholar
CaJacob, D., McCarthy, N., O'Shea, T. and McGwier, R. (2016). Geolocation of RF Emitters with a Formation-Flying Cluster of Three Microsatellites. Proceedings of the 30th AIAA/USU Conference on Small Satellites, USA.Google Scholar
Duchi, J. (2007). Derivations for linear algebra and optimization. http://web.stanford.edu/jduchi/projects/general_notes.pdf, Accessed 10 Jan 2017.Google Scholar
Globalsecurity.org. (1993). The U.S. Navy's “White Cloud” Spaceborne ELINT System. http://www.globalsecurity.org/space/library/report/1993/noss_andronov.htm , Accessed 2 May 2017.Google Scholar
Granstrom, K., Lundquist, C. and Orguner, O. (2012). Extended Target Tracking Using a Gaussian-Mixture PHD Filter. IEEE Transactions on Aerospace and Electronic Systems, 48, 3268–3286.Google Scholar
Granstrom, K. and Orguner, U. (2012). A PHD Filter for Tracking Multiple Extended Targets Using Random Matrices. IEEE Transactions on Signal Processing, 60, 5657–5671.Google Scholar
Hager, J.W., Behensky, J.F., and Drew, B.W. (1989). The Universal Grids: Universal Transverse Mercator (UTM) and Universal Polar Stereographic (UPS). DMA Technical Manual 8358.2, Defence Mapping Agency.Google Scholar
Jain, A.K., Murty, M.N. and Flynn, P.J. (1999). Data Clustering: A Review. ACM Computing Surveys (CSUR), 31, 264–323.Google Scholar
Jazwinski, A.H. (2007). Stochastic Processes and Filtering Theory. Courier Corporation.Google Scholar
Kim, C., Li, F., Ciptadi, A. and Rehg, J.M. (2015). Multiple Hypothesis Tracking Revisited. Proceedings of the IEEE International Conference on Computer Vision, Santiago, CHI.Google Scholar
Koch, J.W. (2008). Bayesian Approach to Extended Object and Cluster Tracking Using Random Matrices. IEEE Transactions on Aerospace and Electronic Systems, 44, 1042–1059.Google Scholar
Lei, G., Tang, B. and Gang, L. (2008). Data Association Based on Target Signal Classification Information. Journal of Systems Engineering and Electronics, 19, 246–251.Google Scholar
Lin, J. (1991). Divergence Measures Based on the Shannon Entropy. IEEE Transactions on Information Theory, 37, 145–151.Google Scholar
Papageorgiou, D. and Salpukas, M. (2009). The Maximum Weight Independent Set Problem for Data Association in Multiple Hypothesis Tracking. Optimization and Cooperative Control Strategies, 381, 235–255.Google Scholar
Reid, D. (1979). An Algorithm for Tracking Multiple Targets. IEEE transactions on Automatic Control, 24, 843–854.Google Scholar
Ren, X., Huang, Z., Sun, S., Liu, D. and Wu, J. (2014). An Efficient MHT Implementation Using GRASP. IEEE Transactions on Aerospace and Electronic Systems, 50, 86–101.Google Scholar
Schuhmacher, D., Vo, B.T. and Vo, B.N. (2008). A Consistent Metric for Performance Evaluation of Multi-Object Filters. IEEE Transactions on Signal Processing, 56, 3447–3457.Google Scholar
Sölver, C.V. and Marcus, G.J. (1958). Dead Reckoning and the Ocean Voyages of the Past. The Mariner's Mirror, 44, 18–34.Google Scholar
Sun, H., Zou, H., Ji, K., Zhou, S. and Lu, C. (2015). Combined Use of Optical Imaging Satellite Data and Electronic Intelligence Satellite Data for Large Scale Ship Group Surveillance. Journal of Navigation, 68, 383–396.Google Scholar
Vivone, G. and Braca, P. (2016). Joint Probabilistic Data Association Tracker for Extended Target Tracking Applied to X-Band Marine Radar Data. IEEE Journal of Oceanic Engineering, 41, 1007–1019.Google Scholar
Vo, B.T., Vo, B.N. and Cantoni, A. (2008). Bayesian Filtering with Random Finite Set Observations. IEEE Transactions on Signal Processing, 56, 1313–1326.Google Scholar
Xia, D., Zou, H., Lei, L. and Zhu, Y. (2014). Track Correlation Algorithm of AIS and Satellite Electronic Information Based on Point-Track Distance. The 11th World Congress on Intelligent Control and Automation, Shenyang, China.Google Scholar
Zhu, Y. and Zhou, S. (2014). GM-PHD Filter with Signal Features of Emitter. Asian Journal of Control, 17, 1978–1983.Google Scholar