Archives
Current issue
About
About us
Aims and Scope
Abstracting and Indexing
Editorial Office
Open Access Policy
Publication Ethics
Journal History
Publisher
Follow us: Twitter
Contact
For Authors
Editorial Policy
Peer Review Policy
Manuscript Preparation Guidelines
Copyright & Licencing
Publication Fees
Fee Waiver Policy for Doctoral Students
EJMSTE Language Editing Service
More
Statistics
Special Issues
Special Issue Announcements
Published Special Issues
A Recognition Approach of Radar Blips Based on Improved Fuzzy C Means
1
College of Marine Sciences, Minjiang University, CHINA
2
The Fujian College’s Research Based of Humanities and Social Science for Internet Innovation Research Center, Minjiang University, CHINA
3
Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, CHINA
4
Wuhan University of Technology, CHINA
5
Department of Physics and Electronic Information Engineering, Minjiang University, CHINA
Online publication date: 2017-08-23
Publication date: 2017-08-23
Corresponding author
Xinglong Liu
Department of Physics and Electronic Information Engineering, Minjiang University, China. Address to No. 200, Xiyuangong Road., Shangjie Town, Minhou County, Fuzhou City 350108, China. Tel: +86-13609549789
Department of Physics and Electronic Information Engineering, Minjiang University, China. Address to No. 200, Xiyuangong Road., Shangjie Town, Minhou County, Fuzhou City 350108, China. Tel: +86-13609549789
EURASIA J. Math., Sci Tech. Ed 2017;13(8):6005-6017
KEYWORDS
ABSTRACT
Maritime radar is the kernel sensor for tracking vessels in Vessel Traffic Service system, it is important for Maritime Situation Awareness. However, the images collected by the maritime radar are inundated with excessive noise blips, which bring variety of troubles in extraction of ship targets from the images. This paper proposes a radar target recognition method based on fuzzy C-means. First, the attributes of the blips in the sequential radar images, such as speed, direction and size, are quantified as three pieces of evidence to determine whether a radar blip is a moving vessel. Then, an artificial intelligence was built based on FCM. According to the three pieces of evidence, the possibility of a blip being a real vessel is computed with FCM. The main difficulty in building the FCM framework is to find an appropriate way to provide the classification coefficient C and the fuzzy coefficient m. Since the C in classification is finite, this study proposes a method to obtain C by assessing the Euclidean distance of the expected result. Since m is related to the discreteness of evidence and results, the coefficient can be assessed by Shannon entropy and gain. Field experiments suggest that the improved FCM is capable of classifying the radar blips accurately, and reducing the operational strength of the ship operators and improving the safety.
REFERENCES (28)
1.
Abonyi, J., Babuska, R., & Szeifert, F. (2002). Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics, 32(5), 612-21. doi:10.1109/TSMCB.2002.1033180.
2.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm Original Research Article. Computers & Geosciences, 10(2-3), 191-203. doi:10.1016/0098-3004(84)90020-7.
3.
Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. US: Springer.
4.
Chan, K. Y., & Haschke, R. H. (1983). Epithelial-stromal interactions: specific stimulation of corneal epithelial cell growth in vitro by a factor(s) from cultured stromal fibroblasts. Experimental Eye Research, 36(2), 231-246. doi:10.1016/0014-4835(83)90008-8.
5.
De Feo, M., Graziano, A., Miglioli, R., & Farina, A. (1997). IMMJPDA versus MHT and Kalman filter with NN correlation: performance comparison. IEE Proceedings-Radar, Sonar and Navigation, 144(2), 49-56. doi:10.1049/ip-rsn:19970976.
6.
Ghosh, A., Mishra, N. S., & Ghosh, S. (2011). Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 181(4), 699-715. doi:10.1016/j.ins.2010.10.016.
7.
IALA Recommendation E-148. (2015). The need to implement regional e-navigation solutions based on international standards, Edition 1.0.
9.
IALA Guideline 1114. (2015). A Technical Specification for the Common Shore-based System Architecture (CSSA) Edition 1.0.
10.
He, X., Bi, Y., & Guo, Y. (2015). Target tracking algorithm of ballistic missile in boost phase based on ground-based radar systems. Journal of Information &Computational Science, 12(2), 855-864. doi:10.12733/jics20105682.
11.
Izakian, H., & Abraham, A. (2011). Fuzzy c-means and fuzzy swarm for fuzzy clustering problem. Expert Systems with Applications, 38(3), 1835-1838. doi:10.1016/j.eswa.2010.07.112.
12.
Kannan, S. R., Ramathilagam, S., & Chung, P. C. (2012). Effective fuzzy c-means clustering algorithms for data clustering problems. Expert Systems with Applications, 39(7), 6292-6300. doi:10.1016/j.eswa.2011.11.063.
13.
Li, Y., & Yu, F. (2009). A New Validity Function for Fuzzy Clustering. International Conference on Computational Intelligence and Natural Computing, 1, 462-465. IEEE Computer Society. doi:10.1109/CINC.2009.100.
14.
Li, R. P., & Mukaidono, M. (1995). Maximum-entropy approach to fuzzy clustering. IEEE International Conference on Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and the Second International Fuzzy Engineering Symposium, 4, 2227-2232. doi:10.1109/FUZZY.1995.409989.
15.
Lin, B., & Huang, C. H. (2006). Comparison between ARPA radar and AIS characteristics for vessel traffic services. Journal of Marine Science & Technology, 14(3), 182-189. doi:10.6119/JMST.
16.
Liu, L., Sun, S. Z., Yu, H., Yue, X., & Zhang, D. (2016). A modified fuzzy c-means (fcm) clustering algorithm and its application on carbonate fluid identification. Journal of Applied Geophysics, 129, 28-35. doi:10.1016/j.jappgeo.2016.03.027.
17.
Ma, F., Chu, X., Yan, X., & Liu, C. (2013). Error distinguish of AIS based on evidence combination. Journal of Theoretical & Applied Information Technology, 45(1), 83-90.
18.
Ma, F., Chu, X. M., & Yan, X. P. (2012). Short message characteristics of AIS base stations. Journal of Traffic & Transportation Engineering, 12(6), 111-118.
19.
Ma, F., Chu, X., & Liu, C. (2014). The Error Distinguishing of Automatic Identification System Based on Improved Evidence Similarity. International Conference on Transportation Information and Safety (pp.715-722). doi:10.1061/9780784413036.096.
20.
Ma, T. Y., & Lebacque, J. P. (2013). A cross entropy based multiagent approach for multiclass activity chain modeling and simulation. Transportation Research Part C Emerging Technologies, 28(3), 116-129. doi:10.1016/j.trc.2011.11.014.
21.
Nanda, S. K., Tripathy, D. P., & Ray, N. K. (2012). Fuzzy-c-mean based radial basis function network application in machinery noise prediction. Procedia Engineering, 38(38), 3596-3602. doi:10.1016/j.proeng.2012.06.416.
22.
Pal, N. R., & Bezdek, J. C. (1995). On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy Systems, 3(3), 370-379. doi:10.1109/91.413225.
23.
Popescu, M., Bezdek, J. C., Havens, T. C., & Keller, J. M. (2012). A cluster validity framework based on induced partition dissimilarity. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 43(1), 308-320. doi:10.1109/TSMCB.2012.2205679.
24.
Roberts, S. J., Holmes, C., & Denison, D. (2001). Minimum-entropy data partitioning using reversible jump Markov chain Monte Carlo. IEEE Transactions on Pattern Analysis & Machine Intelligence, 23(8), 909-914. doi:10.1109/34.946994.
25.
Seixas, F. L., Zadrozny, B., Laks, J., Conci, A., & Saade, D. C. M. (2014). A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer’s disease and mild cognitive impairment. Computers in biology and medicine, 51, 140-158. doi:10.1016/j.compbiomed.2014.04.010.
26.
Su, M. S., Chia, C. C., Chen, C. Y., & Chen, J. F. (2014). Classification of partial discharge events in gilbs using probabilistic neural networks and the fuzzy c-means clustering approach. International Journal of Electrical Power & Energy Systems, 61, 173-179. doi:10.1016/j.ijepes.2014.03.054.
27.
Yoo, J. C., & Kim, Y. S. (2003). Alpha–beta-tracking index (α–β–Λ) tracking filter. Signal Processing, 83(1), 169-180. doi:10.1016/S0165-1684(02)00388-2.
28.
Zheng, Y., Jeon, B., Xu, D., Wu, Q. M., & Zhang, H. (2015). Image segmentation by generalized hierarchical fuzzy C-means algorithm. Journal of Intelligent & Fuzzy Systems, 28(2), 961-973. doi:10.3233/IFS-141378.
| eISSN: | 1305-8223 |
| ISSN: | 1305-8215 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
