Mission scheduling of multi-sensor collaborative observation for space surveillance network

doi:10.23919/JSEE.2023.000104

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (4): 906-923.doi: 10.23919/JSEE.2023.000104

• SYSTEMS ENGINEERING • Previous Articles

Mission scheduling of multi-sensor collaborative observation for space surveillance network

Xi LONG¹(), Weiwei CAI¹^,*(), Leping YANG¹(), Tianyu WANG²()

¹ College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
² School of Traffic and Transportation Engineering, Central South University, Changsha 410073, China

Received:2021-12-22 Online:2023-08-18 Published:2023-08-28
Contact: Weiwei CAI E-mail:longxi_1999@163.com;caiweiwei@nudt.edu.cn;ylp_1964@163.com;wangtianyu189@163.com
About author:
LONG Xi was born in 1999. He received his B.S. degree from Shenyang Aerospace University (SAU), Shenyang, China, in 2020. He is a Ph.D. student with the College of Aerospace Science and Engineering, National University of Defense Technology (NUDT). His research interest is space domain awareness technique. E-mail: longxi_1999@163.com

CAI Weiwei was born in 1987. He received his B.S. and Ph.D. degrees from National University of Defense Technology (NUDT), Changsha, China, in 2009 and 2015, respectively. He is a lecturer with the College of Aerospace Science and Engineering, NUDT. His research interests include space mission planning, dynamics and control of on-orbit operation. E-mail: caiweiwei@nudt.edu.cn

YANG Leping was born in 1964. He received his B.S. and M.S degrees from National University of Defense Technology (NUDT), Changsha, China, in 1984 and 1987, respectively. He is a professor with the College of Aerospace Science and Engineering, NUDT. His research interests include space mission planning, dynamics and control of on-orbit operation. E-mail: ylp_1964@163.com

WANG Tianyu was born in 1998. He received his B.S. degree from Taiyuan University of Technology (TYUT), Taiyuan, China, in 2020. He is an M.S. student with the School of Traffic and Transportation Engineering, Central South University (CSU). His research interests include theory and application of intelligent planning. E-mail: wangtianyu189@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (11802333) and the Scientific Research Program of the National University of Defense Technology (ZK19-31)

Abstract

Abstract:

With increased dependence on space assets, scheduling and tasking of the space surveillance network (SSN) are vitally important. The multi-sensor collaborative observation scheduling (MCOS) problem is a multi-constraint and high-conflict complex combinatorial optimization problem that is non-deterministic polynomial (NP)-hard. This research establishes a sub-time window constraint satisfaction problem (STWCSP) model with the objective of maximizing observation profit. Considering the significant effect of genetic algorithms (GA) on solving the problem of resource allocation, an evolution heuristic (EH) algorithm containing three strategies that focus on the MCOS problem is proposed. For each case, a task scheduling sequence is first obtained via an improved GA with penalty (GAPE) algorithm, and then a mission planning algorithm (heuristic rule) is used to determine the specific observation time. Compared to the model without sub-time windows and some other algorithms, a series of experiments illustrate the STWCSP model has better performance in terms of total profit. Experiments about strategy and parameter sensitivity validate its excellent performance in terms of EH algorithms.

Key words: multi-sensor observation, resource scheduling, sub-time window, evolution heuristic algorithm

Xi LONG, Weiwei CAI, Leping YANG, Tianyu WANG. Mission scheduling of multi-sensor collaborative observation for space surveillance network[J]. Journal of Systems Engineering and Electronics, 2023, 34(4): 906-923.

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References 33

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Parameter	Value
${\rm{Gen}}$	500
${\rm{NP}}$	200
$p_c$	0.8
$p_m$	0.1
$p_{{\rm{Swap}}}$	0.2
$p_{{\rm{Reversion}}}$	0.5
$p_{{\rm{Insertion}}}$	0.3

Target scale	Model without sub-time windows			STWCSP model			EH average result
Target scale	FCFS	IFCFS	GAPE	GAPE-HP	GAPE-HD	GAPE-HR	EH average result
500	518	2257	2449	2515	2496	2554	2521.67
600	554	2307	2760	2882	2974	3024	2960
700	595	2348	3011	3270	3314	3317	3300.33
800	595	2369	3199	3674	3498	3512	3561.33
900	612	2389	3479	3927	4038	4149	4038
1000	640	2399	3726	4142	4135	4233	4170
1100	642	2402	3885	4409	4583	4373	4455
1200	656	2425	4073	4631	4617	4635	4627.67
1300	665	2449	4231	4858	4782	4801	4813.67

Capability	GAPE	GAPE-HP	GAPE-HD	GAPE-HR
(1 1 1)	2128	2676	2660	2516
(1 1 5)	3317	3699	3709	3742
(1 5 1)	3797	4283	4380	4268
(1 5 5)	4939	4874	4645	5015
(2 2 2)	4231	4808	4898	4878
(3 3 3)	4838	5226	5251	5257
(4 4 4)	5108	5339	5358	5389
(5 1 1)	3726	4142	4135	4233
(5 5 5)	5350	5406	5414	5420

Observation	GAPE	GAPE-HP	GAPE-HD	GAPE-HR
[0 100]	3762	4998	4963	4993
[100 200]	3765	4756	4617	4586
[200 300]	3813	4430	4394	4448
[300 400]	3740	4193	4237	4184
[400 500]	3705	3828	3994	3797
[500 600]	3826	3709	3715	3558
[600 700]	3708	3468	3419	3343
[700 800]	3784	3186	3182	3705
[800 900]	3783	2982	2898	2951

Transfer time	GAPE	GAPE-HP	GAPE-HD	GAPE-HR
[20 100 80]	3802	4080	4293	4119
[5 10 10]	3740	4293	4380	4218
[5 100 80]	3726	4142	3650	4233
[5 25 20]	3788	4368	4304	4302
[5 50 40]	3842	4329	4316	4294
[50 100 80]	3645	4110	4149	4219

Mission scheduling of multi-sensor collaborative observation for space surveillance network

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Probability	GAPE	GAPE-HP	GAPE-HD	GAPE-HR
[0.5 0.4 0.3 0.3]	3413	3929	3765	3732
[0.5 0.5 0.4 0.4]	3341	3872	3838	3813
[0.7 0.2 0.3 0.4]	3735	4059	4093	4105
[0.8 0.1 0.2 0.5]	3726	4142	4135	4233
[0.8 0.1 0.3 0.4]	3700	4214	4334	3722
[0.9 0.1 0.4 0.3]	3846	4277	4271	4242

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