TRACK CORRELATION ALGORITHMS BASED ON FUZZY SYNTHETIC FUNCTION

He You; Lu Dajin; Peng Yingning; Wang Guohong

Volume 21 Issue 1

Jan. 1999

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He You, Lu Dajin, Peng Yingning, Wang Guohong. TRACK CORRELATION ALGORITHMS BASED ON FUZZY SYNTHETIC FUNCTION[J]. Journal of Electronics & Information Technology, 1999, 21(1): 91-96.

Citation:

He You, Lu Dajin, Peng Yingning, Wang Guohong. TRACK CORRELATION ALGORITHMS BASED ON FUZZY SYNTHETIC FUNCTION[J]. Journal of Electronics & Information Technology, 1999, 21(1): 91-96.

He You, Lu Dajin, Peng Yingning, Wang Guohong. TRACK CORRELATION ALGORITHMS BASED ON FUZZY SYNTHETIC FUNCTION[J]. Journal of Electronics & Information Technology, 1999, 21(1): 91-96.

Citation:

He You, Lu Dajin, Peng Yingning, Wang Guohong. TRACK CORRELATION ALGORITHMS BASED ON FUZZY SYNTHETIC FUNCTION[J]. Journal of Electronics & Information Technology, 1999, 21(1): 91-96.

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TRACK CORRELATION ALGORITHMS BASED ON FUZZY SYNTHETIC FUNCTION

Received Date: 1997-08-01
Rev Recd Date: 1998-03-09
Publish Date: 1999-01-19

Abstract

Abstract

This paper presents fuzzy synthetic track correlation algorithms by using the synthetic analysis method in modern mathematics. In this paper, the computation and evaluation methods of the fuzzy synthetic measure of similarity between two state estimation vectors are discussed in detail, the recursive forms of three typical fuzzy synthetic functions are derived, the fuzzy synthetic track correlation criterion is described as well. Moreover, the algorithm is compared with two classical methods through simulation. The simulation results show that the performance of the fuzzy synthetic track correlation algorithm is much better than that of the classical methods in dense multitarget environments, more cross, split and maneuvering track situations. Under above situations, its correct correlation rate is improved about 40 percent over that of the classical methods.

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References(1)

References

Bar-Shalom Y. Multisensor Tracking: Advanced Aplication, New York: Artech House, 1990, (1): 187-195.[2]何友.多目标多传感器分布信息融合算法:[博士论文].北京:清华大学,1996, 9-13.[3]何友,谭庆海.多传感器系统分类研究.火力与指挥控制,1988.13(2): 1-10.[4]何友,彭应宁,陆大Jin.多传感器数据融合模型评述.清华大学学报,1996, 36(9): 14-20.[5]孙红岩,毛士艺,林品兴.多传感器数据分层融合的性质.电子学报,1996, 24(6): 55-61.[6]Ditzler W R. A demonstration of multisensor tracking. In Proceedings of the 1987 Tri-Service Data Fusion Symposium. USA: June 1987, 303-311.[7]Bar-Shalom Y. Tracking and Data Association. New York: Academic Press, 1988, 266-272.[8]Gill E. On the track similarity test in track splitting algorithm. IEEE Trans. on AES, 1994, 30(2): 604-606.[9]何友,谭庆海,蒋蓉蓉.多传感器综合系统中的航迹相关算法.火力与指挥控制,1989,14(1): 1-12.[10] Kenefic R J. Local and remote track file registration using minimum description length. IEEE Trans. on AES, 1993, 29(3): 651-655.[10]何友, 彭应宁, 陆大Jin, 高志永.分布式多传感器数据融合中的双门限航迹相关算法.电子科学学刊,1997, 19(6): 721-728.[11]李洪兴, 汪培庄. 模糊数学.北京:国防工业出版社,1994, 54-87.[12]何友, 彭应宁, 陆大Jin,高志永.多传感器数据融合中两种新的航迹相关算法.电子学报,1997, 25(9): 10-14.

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