A New Radar Signal Sorting Method Based on Data Field

Abstract:

With the increasingly complex electromagnetic environment and continuous appearance of advanced system radars, signals received by radar reconnaissance receivers become even more intensive and complex, because scanning time of radar reconnaissance of each direction is very small, composition of the signals received are very complex, number of signals from different radar emitters differ greatly, traditional radar sorting methods can’t process the signals effectively. Aiming at solving the above problem, a novel radar multi-parameter signal sorting method based on data field and hierarchical clustering is proposed. Data field is introduced to reduce compute capacity and determine the parameters of hierarchical clustering. Hierarchical clustering is known that can obtain multi-level clustering structure of different particle size, by which, we can get small number of radar signals drown in many radar signals. Experimental results show that method presented in this paper can sort radar signals in complex electromagnetic environment effectively.

• Periodical: Applied Mechanics and Materials (Volume 610)
• Pages: 401-406
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.610.401
• Online since: August 2014
• Authors: Wan Jian, Wen Ming Song*
• Keywords: Data Field Theory, Hierarchical Clustering, Radar Signal

References

[1] Sun Xin, Hou Hui-qun, Yang Cheng-zhi. 2010, Unknown radar signals deinterleaving based on improved K-means algorithm., 17: 91-96.

[2] Xiang Xian, Tang Jianlong. 2010, A method of radar signal sorting based on grid density clustering., Fire Control Radar Technology. 39(4): 67-72.

[3] Sugeno, M. Yasukawa,T. 2007, A fuzzy-logic-based approach to qualitative modeling, Electronic warfare. 2(2): 9-12.

[4] Bian Qi, Zhang Xuegong. 2000. Pattern recognition., Tsinghua University Press.

[5] Huang Jiangjun, Yang Xun, Xie Weixin. 2007, Cluster cloud model based C-means clustering algorithm., 23(4): 284-287.

[6] Li Deyi, Du Yi, 2005. Artificial intelligence with uncertainty., National Defence Industry Press.

[7] Li Deyi. 2000, Uncertainty in knowledge representation, Engineering Science. 2(10): 73-79.

[8] Gao Xinbo. 2004, Hierarchicalcluster analysis and its application. " Xi, an Electronic and Science University press: 49-59.

[9] Liu Xubo, Si Xicai. 2009, Sorting of radar-signals based on modified hierarchicalclus- tering., Journal of Projectiles, Rockets, Missiles and Guidance., 29(5): 278-282.

[10] Li Deyi, Meng Haijun, Shi Xuemei. 1995, Membership clouds and membership cloud gengerators., Journal of computer research and development. 32(6): 15-20.