IFCEM based recognition method for target with interval-overlapped hybrid attributes

doi:10.23919/JSEE.2022.000131

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (2): 408-421.doi: 10.23919/JSEE.2022.000131

• • 上一篇

收稿日期:2021-12-17 接受日期:2022-07-12 出版日期:2023-04-18 发布日期:2023-04-18

IFCEM based recognition method for target with interval-overlapped hybrid attributes

Xin GUAN¹(), Shuangming LI¹^,²^,*(), Guidong SUN³(), Haibin WANG¹()

¹ Naval Aviation University, Yantai 264001, China
² Unit 92941 of PLA, Huludao 125001, China
³ Unit 32801 of PLA, Beijing 100082, China

Received:2021-12-17 Accepted:2022-07-12 Online:2023-04-18 Published:2023-04-18
Contact: Shuangming LI E-mail:gxtongwin@163.com;aminglishuang@126.com;sdwhsgd@163.com;hesonwhb@163.com
About author:
GUAN Xin was born in 1978. She received her B.E. degree in communication engineering from Liaoning University, Liaoning, China, in 1999. She received her M.E. and Ph.D. degrees in information and communication engineering from Naval Aeronautical Engineering Institute, Yantai, China, in 2002 and 2006, respectively. She is currently a professor in Naval Avation University. Her research interests are intelligent information processing and multi-source information fusion. E-mail: gxtongwin@163.com

LI Shuangming was born in 1986. He received his B.E. degree in automation and M.E. degree in control science and engineering from Naval Aeronautical Engineering Institute, Yantai, China, in 2009 and 2012, respectively. He is pursing his Ph.D. degree in information and communication engineering from Naval Avation University. His research interests include intelligent recognition and uncertain information processing. E-mail: aminglishuang@126.com

SUN Guidong was born in 1989. He received his B.E. and M.E. degrees from Naval Aeronautical Engineering Institute, Yantai, China, in 2012 and 2015, respectively, and Ph.D. degree from Naval Avation University, Yantai, China, in 2018. His research interests include complex electromagnetic environment, information fusion and intelligent decision making. E-mail: sdwhsgd@163.com

WANG Haibin was born in 1982. He received his B.E. degree in electronic information engineering from Harbin Engineering University, Harbin, China, in 2005 and M.E. degree in electronic science and technology from National University of Defense Technology, Changsha, China, in 2007, respectively. He is pursing his Ph.D. degree in information and communication engineering from Naval Avation University. His research interest is uncertain information processing. E-mail: hesonwhb@163.com
First author contact:
Co-first author
Supported by:
This work was supported by the Youth Foundation of the National Science Foundation of China (62001503), the Excellent Youth Scholar of the National Defense Science and Technology Foundation of China (2017-JCJQ-ZQ-003), and the Special Fund for Taishan Scholar Project (ts201712072)

摘要/Abstract

Abstract:

When the attributes of unknown targets are not just numerical attributes, but hybrid attributes containing linguistic attributes, the existing recognition methods are not effective. In addition, it is more difficult to identify the unknown targets densely distributed in the feature space, especially when there is interval overlap between attribute measurements of different target classes. To address these problems, a novel method based on intuitionistic fuzzy comprehensive evaluation model (IFCEM) is proposed. For numerical attributes, targets in the database are divided into individual classes and overlapping classes, and for linguistic attributes, continuous interval-valued linguistic term set (CIVLTS) is used to describe target characteristic. A cloud model-based method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively. An improved inverse weighted kernel fuzzy c-means (IWK-FCM) algorithm is proposed for solution of attribute weight vector. The possibility matrix is applied to determine the identity and category of query target. Finally, a case study composed of parameter sensitivity analysis, recognition accuracy analysis. and comparison with other methods, is taken to verify the superiority of the proposed method.

Key words: intuitionistic fuzzy comprehensive evaluation model (IFCEM), interval overlapping, cloud model, area-based method, inverse weighted kernel fuzzy c-means (IWK-FCM)

. [J]. Journal of Systems Engineering and Electronics, 2023, 34(2): 408-421.

Xin GUAN, Shuangming LI, Guidong SUN, Haibin WANG. IFCEM based recognition method for target with interval-overlapped hybrid attributes[J]. Journal of Systems Engineering and Electronics, 2023, 34(2): 408-421.

图/表 17

参考文献 32

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Radar class	F₁ /MHz	F₂ /μs	F₃ /μs	F_L
R₁	[4 940,5 160]	[3 680,3 750]	[0.6,1.2]	s₋₂
R₂	[5 000,5 220]	[3 630,3 700]	[0.2,0.5]	s₋₁
R₃	[5 100,5 420]	[3 580,3 650]	[0.4,0.7]	s₀
R₄	[5 400,5 520]	[3 730,3 800]	[0.6,0.9]	s₁
R₅	[5 480,5 620]	[3 490,3 600]	[1.0,1.4]	s₂

Object	F₁	F₂	F₃	F_L
Object 1	4 955	3 736	0.60	25% higher than s₋₂, 10% lower than s₋₁
Object 2	5 090	3 635	0.45	15% higher than s₋₁, 18% lower than s₀
Object 3	5 408	3 599	0.65	30% higher than s₀, 5% lower than s₁
Object 4	5 481	3 765	0.77	5% higher than s₁, 20% lower than s₂
Object 5	5 554	3 599	1.04	25% higher than s₁, 10% lower than s₂

Parameter	Proposed cloud model	Cloud model in [11]			Cloud model in [10]
Parameter	Proposed cloud model	Group 1	Group 2	Group 3	Group 1	Group 2	Group 3
Correct recognition ratio	97.75	93.60	89.35	87.95	90.60	92.40	84.15

Method	Correct ratio
Proposed weight method	97.75
Weight method in [11]	6.05
Weight method in [31]	83.55
Mean weight	83.1

Method	Correct ratio
Method	Training data	Testing data
Proposed method	98	97.75
TOPSIS-IFS-CM	77.5	77.80
DS-PCRS-CM	75	76.40
SVM	94.3	94.05
PSO-KELM	100	92.40

IFCEM based recognition method for target with interval-overlapped hybrid attributes

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Measurement error	Proposed method			TOPSIS-IFS-CM			DS-PCRS-CM			PSO-KELM			SVM
Measurement error	Case 1	Case 2	Case 3	Case 1	Case 2	Case 3	Case 1	Case 2	Case 3	Case 1	Case 2	Case 3	Case 1	Case 2	Case 3
2	83.80	54.80	92.00	59.30	47.00	71.35	61.30	53.00	64.55	47.65	58.45	92.4	26.5	37.1	93.90
5	82.95	49.30	90.60	49.95	37.05	70.90	54.30	48.30	62.05	29.5	44.5	92.9	20.1	24.7	93.9
8	81.55	45.30	90.20	43.75	32.35	68.60	53.50	49.10	62.25	23.4	36.65	92.65	19.9	24.15	93.95
12	81.30	41.70	90.05	40.25	28.70	67.95	53.00	52.00	59.90	22.35	29.30	92.45	20.15	22.35	93.85
15	79.10	41.20	89.50	36.25	26.75	67.50	53.80	50.75	58.50	20.55	27.85	92.2	19.85	21.4	93.9
20	78.40	39.20	89.10	35.95	25.50	64.65	52.30	51.60	55.25	20.30	26.95	92.45	20.65	21.3	93.9