Time resource management of OAR based on fuzzy logic priority for multiple target tracking

doi:10.21629/JSEE.2018.04.09

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (4): 742-755.doi: 10.21629/JSEE.2018.04.09

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Time resource management of OAR based on fuzzy logic priority for multiple target tracking

Qinghua HAN¹(), Minghai PAN¹(), Wucai ZHANG¹(), Zhiheng LIANG^2,*()

¹ College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
² Department of Precision Instrument, School of Mechanical Engineering, Tsinghua University, Beijing 100084, China

Received:2017-03-16 Online:2018-08-01 Published:2018-08-30
Contact: Zhiheng LIANG E-mail:hanqinghua123@163.com;panmh@nuaa.edu.cn;wkyjsd@163.com;liangzhiheng@mail.tsinghua.edu.cn
About author:HAN Qinghua received his B.S. degree from Qingdao University of Technology in 2009 and M.S. degree from Nanjing University of Science and Technology in 2012. Now he is a Ph. D. candidate in Nanjing University of Aeronautics and Astronautics. His research interests include radar signal processing, radar resource management and new system radar. E-mail: hanqinghua123@163.com|PAN Minghai received his B.S. degree in 1985 and M.S. degree in 1988 from Harbin Institute of Technology. Now he is a professor and Ph.D. supervisor of the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics. His research interests include radar system design, RF simulation, radar signal processing and new system radar. E-mail: panmh@nuaa.edu.cn|ZHANG Wucai received his B.S. degree from Nanjing University of Aeronautics and Astronautics in 2014. Now he is an M.S. candidate in Nanjing University of Aeronautics and Astronautics. His research interest is radar signal processing. E-mail: wkyjsd@163.com|LIANG Zhiheng received his B.S. degree in 1991 and M.S. degree in 1999 from Beihang University. Now he is an associate professor of the School of Mechanical Engineering, Tsinghua University. His research interests include radio frequency simulation and radar signal processing. E-mail: liangzhiheng@mail.tsinghua.edu.cn
Supported by:
the National Natural Science Foundation of China(61271327);the National Natural Science Foundation of China(61671241);This work was supported by the National Natural Science Foundation of China (61271327; 61671241)

Abstract

Abstract:

For coping with the multiple target tracking in the presence of complex time-varying environments and unknown target information, a time resource management scheme based on chance-constraint programming (CCP) employing fuzzy logic priority is proposed for opportunistic array radar (OAR). In this scheme, the total beam illuminating time is minimized by effective time resource allocation so that the desired tracking performance is achieved. Meanwhile, owing to the randomness of radar cross section (RCS), the CCP is used to balance tracking accuracy and time resource conditioned on the specified confidence level. The adaptive fuzzy logic prioritization, imitating the human decision-making process for ranking radar targets, can realize the full potential of radar. The Bayesian Cramer-Rao lower bound (BCRLB) provides ′ us with a low bound of localization estimation root-mean-square error (RMSE), and equally important, it can be calculated predictively. Consequently, it is employed as an optimization criterion for the time resource allocation scheme. The stochastic simulation is integrated into the genetic algorithm (GA) to compose a hybrid intelligent optimization algorithm to solve the CCP optimization problem. The simulation results show that the time resource is saved strikingly and the radar performance is also improved.

Key words: chance-constraint programming (CCP), fuzzy logic priority, opportunistic array radar (OAR), root-mean-square error (RMSE), Bayesian Cramer-Rao lower bound (BCRLB)

Qinghua HAN, Minghai PAN, Wucai ZHANG, Zhiheng LIANG. Time resource management of OAR based on fuzzy logic priority for multiple target tracking[J]. Journal of Systems Engineering and Electronics, 2018, 29(4): 742-755.

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Fuzzy variable	Fuzzy value
Range	Very close, close, medium-close, medium, medium-far, far, very far
Velocity	Slow, medium, fast
Threat	Low, medium, high
Priority	Very low, low, medium-low, medium, medium-high, high, very high

Rule number	Fuzzy rules
Rule 1	${\rm{If}}({A_1}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {B_1}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {C_1}), {\rm{then}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {D_1};$
Rule 2	${\rm{If}}({A_2}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {B_2}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {C_2}), {\rm{then}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {D_2};$
Rule 3	${\rm{If}}({A_3}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {B_3}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {C_3}), {\rm{then}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {D_3};$
$ \vdots $	$ \vdots $
Rule$n$	${\rm{If}}({A_N}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {B_N}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {C_N}), {\rm{then}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {D_N};$
Fact	${\rm{If}}(A'{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} B'{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\rm{AND}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} C')$
Conclusion	then$D'$

Target	Position/km	Distance/km	Velocity/(m/s)
1	(– 100, 60)	116.62	(86, – 51)
2	(– 90, 90)	127.28	(354, – 354)
3	(– 80, 120)	144.22	(444, – 666)
4	(– 60, 100)	116.62	(– 51, 86)
5	(0, 110)	110	(0, – 600)
6	(60, 92)	109.84	(164, 251)
7	(90, 104)	137.54	(– 458, – 529)
8	(120, 98)	154.93	(– 697, – 569)
9	(120, 47)	128.88	(– 186, – 73)

Time resource management of OAR based on fuzzy logic priority for multiple target tracking

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