Sensors deployment optimization in multi-dimensional space based on improved particle swarm optimization algorithm

doi:10.21629/JSEE.2018.05.09

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (5): 969-982.doi: 10.21629/JSEE.2018.05.09

• Systems Engineering • Previous Articles Next Articles

Sensors deployment optimization in multi-dimensional space based on improved particle swarm optimization algorithm

Mingnan TANG^1,²(), Shijun CHEN^2,*(), Xuehe ZHENG³(), Tianshu WANG¹(), Hui CAO²()

¹ School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
² Beijing Institute of Electronic System Engineering, Beijing 100854, China
³ The Second Academy of China Aerospace Science and Industry Corporation, Beijing 100854, China

Received:2017-07-17 Online:2018-10-26 Published:2018-11-14
Contact: Shijun CHEN E-mail:tmn1014@163.com;csj19872006@163.com;zhengxuehe@163.com;tswang@tsinghua.edu.cn;caohui314@126.com
About author:TANG Mingnan was born in 1982. Currently he is a researcher in Beijing Institute of Electronic System Engineering. He received his B.S. and M.S. degrees from the School of Astronautics, Beihang University in 2005 and 2008 respectively, majored in aerocraft design. He is currently a Ph.D. candidate in Tsinghua University. His research interests include system design and simulation. E-mail: tmn1014@163.com|CHEN Shijun was born in 1987. He received his B.S. and M.S. degrees from the School of Automation, Beijing Institute of Technology in 2010 and 2013, respectively. Currently he is an engineer in Beijing Institute of Electronic System Engineering. His present research interests include control theory and system design. E-mail: csj19872006@163.com|ZHENG Xuehe was born in 1968. He received his B.S. degree from the Department of Electronics Information Engineering, Tianjin University in 1990, majored in radio guidance. He received his M.S. degree from the School of Information and Electronics, Beijing Institute of Technology in 1995, majored in communication and information system. He received his Ph.D. degree from Beijing Institute of Electronic System Engineering in 2009, majored in guidance and control. Currently he is a researcher in The Second Academy of China Aerospace Science and Industry Corporation Limited. His research interests include system design and simulation. E-mail: zhengxuehe@163.com|WANG Tianshu was born in 1969. He received his B.E. degree from the Department of Engineering Mechanics, Harbin Institute of Technology in 1991, majored in engineering mechanics. He received his M.S. degree from the School of Astronautics, Harbin Institute of Technology in 1994, majored in aircraft design. He received his Ph.D. degree from Harbin Institute of Technology in 1999, majored in aircraft design. Currently he is a professor in Tsinghua University. His research interest is aircraft design. E-mail: tswang@tsinghua.edu.cn|CAO Hui was born in 1986. She received her B.S. degree from Department of Electronics Information Engineering, Sichuan University in 2008, majored in electronics information engineering. She received her M.S. degree from Academy of Opto-Electronic, Chinese Academy of Sciences in 2008, majored in master of signal processing. She is an engineer in Beijing Institute of Electronic System Engineering. Her present research interest is system design. E-mail: caohui314@126.com

Abstract

Abstract:

Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space. Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment. Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system (e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature. For uncovering the optimal deployment of the sensor network, the particle swarm optimization (PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method.

Key words: spatial sensor, optimized deployment strategy, particle swarm optimization (PSO)

Mingnan TANG, Shijun CHEN, Xuehe ZHENG, Tianshu WANG, Hui CAO. Sensors deployment optimization in multi-dimensional space based on improved particle swarm optimization algorithm[J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 969-982.

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Height layer	Evaluation item	Sensor initialization deployment	Sensor optimal deployment
First height layer	Detection area coverage coefficient	0.451 5	0.889 7
	High detection probability coverage coefficient	0.416 2	0.719 3
	Space overlapping coverage coefficient	0.187 9	0.639 9
	Resource utilization coefficient	0.970 5	1
	Weighted evaluation value	0.484 5	0.843 2
Second height layer	Detection area coverage coefficient	0.848 0	0.985 0
	High detection probability coverage coefficient	0.694 0	0.875 8
	Space overlapping coverage coefficient	0.588 5	0.862 7
	Resource utilization coefficient	0.656 4	0.999 2
	Weighted evaluation value	0.757 2	0.952 4
Third height layer	Detection area coverage coefficient	0.933 1	0.978 6
	High detection probability coverage coefficient	0.749 6	0.805 8
	Space overlapping coverage coefficient	0.699 4	0.788 6
	Resource utilization coefficient	0.647 9	0.997 2
	Weighted evaluation value	0.827 8	0.927 0
Global optimization deployment	Comprehensive evaluation value	0.723 8	0.922 9

Experiment number	Initial status	Standard PSO result	Improved PSO result
1	0.741 1	0.937 0	0.991 7
2	0.633 5	0.930 1	0.989 1
3	0.632 3	0.887 6	0.992 0
4	0.679 5	0.937 7	0.980 9
5	0.669 9	0.928 9	0.990 0
6	0.489 0	0.953 3	0.980 6
7	0.657 3	0.971 8	0.992 2
8	0.643 6	0.931 1	0.989 8
9	0.660 6	0.947 4	0.994 8
10	0.616 0	0.936 6	0.986 1

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Sensors deployment optimization in multi-dimensional space based on improved particle swarm optimization algorithm

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Serial number	X/km	Y/km	Z/km	Detection radius/km
1	25	40	40	30
2	30	50	50	20
3	60	35	35	30
4	42	38	38	35
5	65	55	45	50
6	70	40	30	25
7	30	40	60	40
8	38	45	62	55

Serial number	X/ km	Y/ km	Z/ km	Detection radius/km	Type of detections	Cone height/km
1	12	32	120	60	Sky-based (cone)	90
2	23	5	115	50	Sky-based (cone)	70
3	34	54	22	35	Near space (hemisphere)
4	22	34	55	40	Near space (hemisphere)
5	43	21	12	40	Land-based (cone)	20
6	21	25	30	50	Land-based (cone)	25
7	13	21	33	50	Floating air ball (sphere)
8	22	23	12	40	Floating air ball (sphere)