Joint waveform selection and power allocation algorithm in manned/unmanned aerial vehicle hybrid swarm based on chance-constraint programming

doi:10.23919/JSEE.2022.000054

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (3): 551-562.doi: 10.23919/JSEE.2022.000054

收稿日期:2020-12-25 出版日期:2022-06-18 发布日期:2022-06-24

Joint waveform selection and power allocation algorithm in manned/unmanned aerial vehicle hybrid swarm based on chance-constraint programming

Yuanshi ZHANG¹(), Minghai PAN¹(), Weijun LONG²(), Hua LI¹(), Qinghua HAN^3,*()

¹ College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
² Nanjing Research Institute of Electronics Technology, Nanjing 210039, China
³ College of Artificial Intelligence, Zaozhuang University, Zaozhuang 277160, China

Received:2020-12-25 Online:2022-06-18 Published:2022-06-24
Contact: Qinghua HAN E-mail:z_yuanshi@163.com;panmh@nuaa.edu.cn;chinacohit2020@163.com;zibel@163.com;hanqinghua123@163.com
About author:|ZHANG Yuanshi was born in 1995. He is currently working toward his Ph.D. degree in the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. His research interests include multistatic radar, radar resource allocation and target tracking. E-mail: z_yuanshi@163.com||PAN Minghai was born in 1962. He received his B.S. degree in 1985 and his M.S. degree in 1988, both from the 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, radio frequency simulation, radar signal processing and new system radar. E-mail: panmh@nuaa.edu.cn||LONG Weijun was born in 1962. He received his Ph.D. degree from Nanjing University of Aeronautics and Astronautics. Now, he is a researcher in Nanjing Research Institute of Electronics Technology. His research interests include radar signal processing, radar resource management and new system radar. E-mail: chinacohit2020@163.com||LI Hua was born in 1979. He is currently working toward his Ph.D. degree in the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. His research interests include radio frequency simulation and radar signal processing. E-mail: zibel@163.com||HAN Qinghua was born in 1979. He received his B.S. degree from the Qingdao University of Technology in 2009, his M.S. degree from Nanjing University of Science and Technology in 2012, and his Ph.D. degree form Nanjing University of Aeronautics and Astronautics. Now he is lecturer of the College of Artificial Intelligence, Zaozhuang University. His research interests include radar signal processing, radar resource management and new system radar. E-mail: hanqinghua123@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62071440; 61671241).

摘要/Abstract

Abstract:

In this paper, we propose a joint waveform selection and power allocation (JWSPA) strategy based on chance-constraint programming (CCP) for manned/unmanned aerial vehicle hybrid swarm (M/UAVHS) tracking a single target. Accordingly, the low probability of intercept (LPI) performance of system can be improved by collaboratively optimizing transmit power and waveform. For target radar cross section (RCS) prediction, we design a random RCS prediction model based on electromagnetic simulation (ES) of target. For waveform selection, we build a waveform library to adaptively manage the frequency modulation slope and pulse width of radar waveform. For power allocation, the CCP is employed to balance tracking accuracy and power resource. The Bayesian Cramér-Rao lower bound (BCRLB) is adopted as a criterion to measure target tracking accuracy. The hybrid intelligent algorithms, in which the stochastic simulation is integrated into the genetic algorithm (GA), are used to solve the stochastic optimization problem. Simulation results demonstrate that the proposed JWSPA strategy can save more transmit power than the traditional fixed waveform scheme under the same target tracking accuracy.

Key words: multistatic radar system (MRS), target tracking, manned/unmanned aerial vehicle hybrid swarm (M/UAVHS), power allocation, waveform selection

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 551-562.

Yuanshi ZHANG, Minghai PAN, Weijun LONG, Hua LI, Qinghua HAN. Joint waveform selection and power allocation algorithm in manned/unmanned aerial vehicle hybrid swarm based on chance-constraint programming[J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 551-562.

图/表 19

参考文献 34

1	LI W, CHEN J. Review and prospect of cooperative combat of manned/unmanned aerial vehicle hybrid formation. Aerospace Control, 2017, 35(3): 90-96. (in Chinese)
2	GAO X, XIONG J Concept and relevant technologies of cooperative operation for manned-unmanned aerial vehicles. Telecommunication Engineering, 2014, 54 (12): 1612- 1616.
3	IVASHKO I, KRASNOV O, YAROVOY A Performance analysis of multisite radar systems. Proc. of the European Radar Conference, 2013, 459- 462.
4	LONG W J. Opportunity array radar. Beijing: National Defense Industry Press, 2018. (in Chinese)
5	SKOLNIK M. Radar handbook. Nanjing Research Institute of Electromic Technology trans. Beijing: Publishing House of Electronic Industry, 2010. (in Chinese)
6	YAN J K, LIU H W, PU W Q, et al. Joint beam selection and power allocation for multiple target tracking in netted colocated MIMO radar system. IEEE Trans. on Signal Processing, 2016, 64 (24): 6417- 6427. doi: 10.1109/TSP.2016.2607147
7	YAN J K, LIU H W, BO J, et al. Power allocation algorithm for target tracking in unmodulated continuous wave radar network. IEEE Sensors Journal, 2015, 15 (2): 1098- 1108. doi: 10.1109/JSEN.2014.2360039
8	YUAN Y, YI W, KIRUBARAJAN T, et al. Scaled accuracy based power allocation for multi-target tracking with colocated MIMO radars. Signal Processing, 2019, 158, 227- 240. doi: 10.1016/j.sigpro.2019.01.014
9	YI W, YUAN Y, HOSEINNEZHAD R, et al. Resource scheduling for distributed multi-target tracking in netted colocated MIMO radar systems. IEEE Trans. on Signal Processing, 2020, 68, 1602- 1617. doi: 10.1109/TSP.2020.2976587
10	HAN Q H, PAN M H, ZHANG W C, et al. Time resource management of OAR based on fuzzy logic priority for multiple target tracking. Journal of Systems Engineering and Electronics, 2018, 29 (4): 742- 755. doi: 10.21629/JSEE.2018.04.09
11	ZHANG Y S, PAN M H, HAN Q H Joint sensor selection and power allocation algorithm for multiple-target tracking of unmanned cluster based on fuzzy logic reasoning. Sensors, 2020, 20 (5): 1371- 1391. doi: 10.3390/s20051371
12	SHI C G, DING L T, WANG F, et al. Low probability of intercept-based collaborative power and bandwidth allocation strategy for multi-target tracking in distributed radar network system. IEEE Sensors Journal, 2020, 20 (12): 6367- 6377. doi: 10.1109/JSEN.2020.2977328
13	KLANIL M B, NIJSURE Y, GAGNON G, et al. Cognitive waveform and receiver selection mechanism for multistatic radar. IET Radar, Sonar & Navigation, 2015, 10 (2): 417- 425.
14	WANG J T, QIN Y L, WANG H Q, et al. Dynamic waveform selection for maneuvering target tracking in clutter. IET Radar, Sonar & Navigation, 2013, 7 (7): 815- 825. doi: 10.1049/iet-rsn.2012.0310
15	ZHANG K Y, YU J P, PENG X Y, et al. Radar adaptive waveform selection behavior recognition based on neural network. Proc. of the IEEE 3rd International Conference on Electronics Technology, 2020, 699- 703.
16	ZHANG Z K, SALOUS S, ZHU J H, et al. A novel waveform selection method for cognitive radar during target tracking based on the wind driven optimization technique. Proc. of the IET International Radar Conference, 2015. DOI: 10.1049/cp.2015.1106.
17	NGUYEN N H, ARABLOUEI R, DOGANCAY K Adaptive distributed waveform selection for target tracking by a multistatic radar system. Proc. of the IEEE Workshop on Statistical Signal Processing, 2014, 149- 152.
18	SKOLNIK M. Introduction to radar system. New York: McGraw-Hill Companies, 2001.
19	GODRICH H, PETROPULU A P, VINCENT P H Sensor selection in distributed multiple radar architectures for localization: a knapsack problem formulation. IEEE Trans. on Signal Processing, 2012, 60 (1): 247- 260. doi: 10.1109/TSP.2011.2170170
20	CHEN H W, TA S Y, SUN B Cooperative game approach to power allocation for target tracking in distributed MIMO radar sensor networks. IEEE Sensor Journal, 2015, 15 (10): 5423- 5432. doi: 10.1109/JSEN.2015.2431261
21	XIE M C, YI W, KIRUBARAJAN T, et al. Joint node selection and power allocation strategy for multitarget tracking in decentralized radar networks. IEEE Trans. on Signal Processing, 2018, 66 (3): 729- 743. doi: 10.1109/TSP.2017.2777394
22	GARCIA N, COULON M, LOPS M, et al. Resource allocation in radar networks for non-coherent localization. Proc. of the IET International Conference on Radar Systems, 2012. DOI: 10.1049/cp.2012.1670.
23	YAN J K, LIU H W, JIU B, et al Simultaneous multibeam resource allocation scheme for multiple target tracking. IEEE Trans. on Signal Processing, 2015, 63 (12): 3110- 3112.
24	HAN Q H, PAN M H, LIANG Z H Joint power and beam allocation of opportunistic array radar for multiple target tracking in clutter. Digital Signal Processing, 2018, 78, 136- 151. doi: 10.1016/j.dsp.2018.03.007
25	LIU B D. Theory and practice of uncertain programming. Berlin: Springer, 2009.
26	LONG W J, BEN D, PAN M H Pattern synthesis for opportunistic array radar using least square fitness estimation-genetic algorithm method. International Journal of RF and Microwave Computer-Aided Engineering, 2011, 21 (5): 584- 588. doi: 10.1002/mmce.20545
27	KERSHAW D J, EVANS R J Optimal waveform selection for tracking systems. IEEE Trans. on Information Theory, 1994, 40 (5): 1536- 1550. doi: 10.1109/18.333866
28	VAN T H L. Detection, estimation, modulation theory. New York: Wiley, 1971.
29	LIU J, FANG N, XIE Y J, WANG B F. Dynamic target RCS characteristic analysis under the influence of attitude perturbation. Journal of Systems Engineering and Electronics, 2015, 37(4) : 775-781. (in Chinese)
30	ZHANG W, WANG G Y, ZENG Y H, et al. Distribution characteristic of airplane dynamic RCS. Chinese Journal of Radio Science, 2010, 25(1): 117-121. (in Chinese)
31	VAN TREES H L, BELL K L. Bayesian bounds for parameter estimation and nonlinear filtering/tracking. New York: Wiley, 2007.
32	GLASS J D, SMITH L D MIMO radar resource allocation using posterior Cramér-Rao lower bounds. Proc. of the Aerospace Conference, 2011, 1- 9.
33	ZHANG X Y. Dynamic economic dispatch incorporating multiple wind farms based on chance constrained programming. Hangzhou: Zhejiang University, 2017. (in Chinese)
34	HUANG Y P, ZHOU Y F, ZHANG H B, TANG X H. Algorithm of weighting factors dynamic allocation in multi-radar track weighted fusion. Computer Applications, 2008, 29(9): 2452-2454. (in Chinese)

${b_{{\rm{m i n}}} }$	$ {b_{{\rm{m a x}}}} $	$ \Delta b $ /(GHz/s)	$ {\lambda _{{\rm{m i n}}}} $	$ {\lambda _{{\rm{m a x}}}} $	$ \Delta \lambda $ /μ ${\rm{ s} }$
1e10	1e11	$ {\text{2}} $	5e?5	10e?5	5

Index	FM slope	Pulse width
Waveform 1	5.8e10	7.0e?5
Waveform 2	4.4e10	8.5e?5
Waveform 3	6.2e10	6.5e?5

Joint waveform selection and power allocation algorithm in manned/unmanned aerial vehicle hybrid swarm based on chance-constraint programming

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