Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

doi:10.21629/JSEE.2018.04.10

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (4): 756-767.doi: 10.21629/JSEE.2018.04.10

收稿日期:2017-06-26 出版日期:2018-08-01 发布日期:2018-08-30

Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

Yun ZHONG^1,*(), Peiyang YAO¹(), Jieyong ZHANG¹(), Lujun WAN²()

¹ Information and Navigation College, Air Force Engineering University, Xi'an 710077, China
² Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China

Received:2017-06-26 Online:2018-08-01 Published:2018-08-30
Contact: Yun ZHONG E-mail:718227697@qq.com;ypy_664@163.com;dumu3110728@126.com;pandawlj@126.com
About author:ZHONG Yun was born in 1990. He is currently a Ph.D. student of Air Force Engineering University. He received his B.S. degree in network engineering, M.S. degree in communication and information system from Air Force Engineering University, in 2012 and 2014, respectively. His research interests include decision support system and MAV/UAV cooperative engagement. E-mail: 718227697@qq.com|YAO Peiyang was born in 1960. He is currently a professor and Ph.D. student supervisor of Air Force Engineering University. His research interests include command information system and combat modeling. E-mail: ypy_664@163.com|ZHANG Jieyong was born in 1983. He is currently a lecturer of Information and Navigation College, Air Force Engineering University. His research interests include mission planning technique and military organizational analysis. E-mail: dumu3110728@126.com|WAN Lujun was born in 1986. He is currently a lecturer of Air Force Engineering University. His research interests include operational organization design and air traffic control. E-mail: pandawlj@126.com
Supported by:
the National Natural Science Foundation of China(61573017);the Doctoral Innovation Found of Air Force Engineering University(KGD08101604);This work was supported by the National Natural Science Foundation of China (61573017), and the Doctoral Innovation Found of Air Force Engineering University (KGD08101604)

摘要/Abstract

Abstract:

Manned combat aerial vehicles (MCAVs), and unmanned combat aerial vehicles (UCAVs) together form a cooperative engagement system to carry out operational mission, which will be a new air engagement style in the near future. On the basis of analyzing the structure of the MCAV/UCAV cooperative engagement system, this paper divides the unique system into three hierarchical levels, respectively, i.e., mission level, task-cluster level and task level. To solve the formation and adjustment problem of the latter two levels, three corresponding mathematical models are established. To solve these models, three algorithms called quantum artificial bee colony (QABC) algorithm, greedy strategy (GS) and two-stage greedy strategy (TSGS) are proposed. Finally, a series of simulation experiments are designed to verify the effectiveness and superiority of the proposed algorithms.

Key words: manned combat aerial vehicle (MCAV), unmanned combat aerial vehicle (UCAV), cooperative engagement system, quantum artificial bee colony (QABC), greedy strategy (GS), twostage greedy strategy (TSGS)

. [J]. Journal of Systems Engineering and Electronics, 2018, 29(4): 756-767.

Yun ZHONG, Peiyang YAO, Jieyong ZHANG, Lujun WAN. Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system[J]. Journal of Systems Engineering and Electronics, 2018, 29(4): 756-767.

图/表 14

参考文献 28

1	EVERS L, BARROS A I, MONSUUR H, et al. Online stochastic UAV mission planning with time windows and timesensitive targets. European Journal of Operational Research, 2014, 238 (1): 348- 362.
2	MURRAY C C, PARK W. Incorporating human factor considerations in unmanned aerial vehicle routing. IEEE Trans. on Systems, Man and Cybernetics: Systems, 2013, 43(4): 860-874.
3	ZHU X P, LIU Z C, YANG J. Model of collaborative UAV swarm toward coordination and control mechanisms study. Proc. of the International Conference on Computational Science, 2015: 493-502.
4	ERIGNAC C. An exhaustive swarming search strategy based on distributed pheromone maps. Proc. of the AIAA Infotech & Aerospace Conference and Exhibit, 2007: 1-16.
5	SCHOUWENAARS T, VALENTI M, FERON E, et al. Linear programming and language processing for human/unmanned aerial vehicle team missions. Journal of Guidance, Control and Dynamics, 2006, 29 (2): 303- 313. doi: 10.2514/1.13162
6	CHINGTHAM T, SAHOO G, GHOSE M. An unmanned aerial vehicle as human-assistant robotics system. Proc. of the IEEE International Conference on Computational Intelligence and Computing Research, 2010: 1-6.
7	HU X X, MA H W, YE Q S, et al. Hierarchical method of task assignment for multiple cooperating UAV teams. Journal of Systems Engineering and Electronics, 2015, 26 (5): 1000- 1009. doi: 10.1109/JSEE.2015.00109
8	ZENG J, YANG X K, YANG L Y, et al. Modeling for UAV resource scheduling under mission synchronization. Journal of Systems Engineering and Electronics, 2010, 21 (5): 821- 826. doi: 10.3969/j.issn.1004-4132.2010.05.016
9	LIN L, SUN Q B, WANG S G, et al. Research on time window based coalition formation for multi-UAVs task assignment. Journal of Electronics & Information Technology, 2013, 35 (8): 1983- 1988.
10	HAN X, MANDAL S, PATTIPATI K R, et al. An optimizationbased distributed planning algorithm: a blackboard-based collaborative framework. IEEE Trans. on Systems, Man and Cybernetics: Systems, 2014, 44(6): 673-686.
11	WANG L, QIN Y, XU J, et al. A fuzzy optimization model for high-speed railway timetable rescheduling. Discrete Dynamics in Nature and Society, 2012. doi: 10.1155/2012/827073
12	ARIANO A D, CORMAN F, PACCIARELLI D, et al. Reordering and local rerouting strategies to manage train traffic in real time. Transportation Science, 2008, 42 (4): 405- 419. doi: 10.1287/trsc.1080.0247
13	MANATHARA J G, SUJIT P B, BEARD R W. Multiple UAV coalitions for a search and prosecute mission. Journal of International Robotic System, 2011, 62 (1): 125- 158. doi: 10.1007/s10846-010-9439-2
14	SUJIT P B, MANATHARA J G, GHOSE D, et al. Handbook of unmanned aerial vehicles. New York: Springer Verlag, 2015.
15	KIM M H, BAIK H, LEE S. Resource welfare based task allocation for UAV team with resource constraints. Journal of Intelligent & Robot System, 2015, 77 (3): 611- 627. doi: 10.1007/s10846-014-0088-8
16	YOUNG B W. Future integrated fire control. Proc. of the 10th International Command and Control Research and Technology Symposium, 2005: 1-21.
17	MENG L H, XU X H, ZHAO Y F. Cooperative coalition for formation flight scheduling based on incomplete information. Chinese Journal of Aeronautics, 2015, 28 (6): 1747- 1757. doi: 10.1016/j.cja.2015.10.007
18	LIN L, ZHENG Z Q. Combinatorial bids based multi-robot task allocation method. Proc. of the IEEE International Conference on Intelligent Robots and Automation, 2005: 18-22.
19	SUJIT P B, GEORGE J M, BEARD R W. Multiple UAV coalition formation. Proc. of the American Control Conference, 2008: 2010-2015.
20	JIN Y, LIAO Y, MINAI A, et al. Balancing search and target response in cooperative UAV teams. IEEE Trans. on Systems, Man and Cybernetics: Systems, 2006, 36(3): 571-587.
21	CHOI H L, BRUNET L, HOW J P. Consensus-based decentralized auctions for robust task allocation. IEEE Trans. on Robotics, 2009, 25(4): 912-926.
22	JOHNSON L, PONDA S S, CHOI H L, et al. Asynchronous decentralized task allocation for dynamic environments. Proc. of the AIAA Guidance, Navigation and Control Conference, 2011: 1441-1453.
23	KARABOGA D. An idea based on honey bee swarm for numerical optimization. Kayseri, Turkey: Erciyes University, 2005.
24	MU L. Dynamic adaptive optimization methodology of C2 organization structure under uncertainty mission environment. Changsha, China: National University of Defense Technology, 2011. (in Chinese)
25	ZHU B, ZHU J F. Flight schedule recovery under uncertain airport capacity. Transactions of Nanjing University of Aeronautics and Astronautics, 2016, 33 (4): 479- 490.
26	PARK C, PATTIPATI K R, AN W. Quantifying the impact of information and organizational structures via distributed auction algorithm: point-to-point communication structure. IEEE Trans. on Systems, Man and Cybernetics: Systems, 2012, 42(1): 68-86.
27	LABOUDI Z, CHIKHI S. Comparison of genetic algorithm and quantum genetic algorithm. The International Arab Journal of Information Technology, 2012, 9 (3): 243- 249.
28	ROSENBERG B, BURGE J, GONSALVES P G. Applying evolutionary multi-objective optimization to mission planning for time-sensitive targets. Proc. of the Genetic and Evolutionary Computation Conference, 2005: 1-8.

$y_k^j$	$b_k^j$	$f(y){>}f(b)$	$\Delta \theta _{jk}^{g+1}$	$s(\alpha _k , \beta _k)$
$y_k^j$	$b_k^j$	$f(y){>}f(b)$	$\Delta \theta _{jk}^{g+1}$	${\alpha _k}{\beta _k}{\rm{ > }}0$	${\alpha _k}{\beta _k}{\rm{ < }}0$	$\alpha _k { = }0$	$\beta _k { = }0$
0	0	false	*	0	0	0	0
0	0	true	*	0	0	0	0
0	1	false	Δ	+1	-1	0	±1
0	1	true	Δ	-1	+1	±1	0
-1	0	false	Δ	-1	+1	±1	0
1	0	true	Δ	+1	-1	0	±1
1	1	false	*	0	0	0	0
1	1	true	*	0	0	0	0

Task	Resource demand
Task	${r_1}$	${r_2}$	${r_3}$	${r_4}$	${r_5}$	${r_6}$	${r_7}$	${r_8}$
$i{t_1}$	5	2	6	0	0	0	0	1
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$i{t_4}$	0	0	7	0	1	0	0	0
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$i{t_7}$	1	4	7	0	0	0	0	0
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$i{t_{10}}$	0	0	0	0	5	0	0	0
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$i{t_{13}}$	6	4	0	0	7	2	3	6
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$i{t_{16}}$	7	1	2	0	0	0	6	0
$i{t_{17}}$	0	0	7	0	0	0	0	0
$i{t_{18}}$	0	0	2	6	2	2	0	0

UCAV	Resource capability
UCAV	${r_1}$	${r_2}$	${r_3}$	${r_4}$	${r_5}$	${r_6}$	${r_7}$	${r_8}$
$u{_1}$	1	0	0	0	5	9	3	0
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$u{_5}$	0	8	6	0	2	0	0	0
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$u{_{10}}$	1	0	8	7	9	0	0	0
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$u{_{15}}$	9	0	0	6	4	6	1	1
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$u{_{20}}$	0	0	9	0	4	1	0	0

Task	Location
$i{t_1}$	(70, 15)
$i{t_2}$	(44, 75)
$i{t_3}$	(15, 40)
$i{t_4}$	(30, 95)
$i{t_5}$	(28, 73)
$i{t_6}$	(24, 60)
$i{t_7}$	(40, 60)
$i{t_8}$	(60, 30)
$i{t_9}$	(15, 25)
$i{t_{10}}$	(45, 35)
$i{t_{11}}$	(35, 50)
$i{t_{12}}$	(25, 30)
$i{t_{13}}$	(15, 40)
$i{t_{14}}$	(20, 45)
$i{t_{15}}$	(30, 10)
$i{t_{16}}$	(56, 15)
$i{t_{17}}$	(84, 35)
$i{t_{18}}$	(43, 25)
-	-
-	-

UCAV	Position
${u_1}$	(90, 55)
${u_2}$	(90, 55)
${u_3}$	(90, 55)
${u_4}$	(90, 55)
${u_5}$	(90, 55)
${u_6}$	(80, 65)
${u_7}$	(80, 65)
${u_8}$	(80, 65)
${u_9}$	(80, 65)
${u_{10}}$	(80, 65)
${u_{11}}$	(70, 75)
${u_{12}}$	(70, 75)
${u_{13}}$	(70, 75)
${u_{14}}$	(70, 75)
${u_{15}}$	(70, 75)
${u_{16}}$	(60, 85)
${u_{17}}$	(60, 85)
${u_{18}}$	(60, 85)
${u_{19}}$	(60, 85)
${u_{20}}$	(60, 85)

Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

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Comparison item	TSGS	MOQABC
Average	2.392 2	19.826 3
Mean square error	0.052 4	31.495 5

Algorithm	QABC	QGA	PTCFA
Object	81.797 8	89.927 4	150.722 6