基于改进D-S证据理论的相控阵雷达作战效能评估

doi:10.12305/j.issn.1001-506X.2024.04.21

摘要/Abstract

摘要：

针对相控阵雷达作战效能评估中战场环境等不确定因素的影响, 采用改进Dempster-Shafer(D-S)证据理论对其进行作战效能评估方法的优化。首先, 通过构建相控阵雷达作战效能评估指标体系, 明确评估指标并赋值权。其次, 引入Kulsinski差异和模糊熵方法, 对传统的D-S证据理论进行改进, 减少指标在融合过程中的融合权重不确定性的影响。最后, 通过改进D-S证据理论对评估指标进行数据融合, 建立评估准确性较高的相控阵雷达作战效能评估模型。通过算例验证分析, 所提方法能够有效减少融合过程中的不确定性影响, 可实现相控阵雷达作战效能评估的目的, 提高作战效能评估精度。

关键词: 相控阵雷达, Dempster-Shafer(D-S)证据理论, 作战效能评估, Kulsinski差异, 模糊熵

Abstract:

Aiming at the influence of uncertain factors such as battlefield environment in phased array radar operational effectiveness evaluation, the improved Dempster-Shafer (D-S) evidence theory is adopted to optimize the operational effectiveness evaluation method. Firstly, the evaluation index system of operational effectiveness of phased array radar is established, and the evaluation index is defined and the assignment weight is given. Secondly, Kulsinski difference and fuzzy entropy method are introduced to improve the traditional D-S evidence theory and reduce the influence of the uncertainty of the fusion weight in the indexes fusion process. Finally, by improving the D-S evidence theory to fuse the evaluation indexes data, the operational effectiveness evaluation model of phased array radar with high accuracy is established. Through the verification and analysis of numerical examples, the proposed method can effectively reduce the influence of uncertainty in the fusion process, which achieves the objective of phased array radar operational effectiveness evaluation, and improve the accuracy of operational effectiveness evaluation.

Key words: phased array radar, Dempster-Shafer (D-S) evidence theory, operational effectiveness evaluation, Kulsinski difference, fuzzy entropy

中图分类号:

TP18

郑丽莎, 尹东亮, 王旋. 基于改进D-S证据理论的相控阵雷达作战效能评估[J]. 系统工程与电子技术, 2024, 46(4): 1330-1336.

Lisha ZHENG, Dongliang YIN, Xuan WANG. Operational effectiveness evaluation of phased array radar based on improved D-S evidence theory[J]. Systems Engineering and Electronics, 2024, 46(4): 1330-1336.

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参考文献 33

1	ZOU H B , WU S S , TIAN M X . Radar quantitative precipitation estimation based on the gated recurrent unit neural network and echo-top data[J]. Advances in Atmospheric Sciences, 2023, 40 (6): 1043- 1057. doi: 10.1007/s00376-022-2127-x
2	KATSILIERIS F , KRACH B . Cross-platform radar resource management for coordinated search and tracking[J]. IEEE Aero-space and Electronic Systems Magazine, 2022, 37 (4): 22- 29. doi: 10.1109/MAES.2021.3052312
3	LI J C , GOU S P , LI R M , et al. Ship segmentation via encoder-decoder network with global attention in high-resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 4016605.
4	WANG D C, ZHANG F, MA F, et al. SAD: a large-scale dataset towards airport detection in synthetic aperture radar images[EB/OL]. [2022-10-20]. http://arxiv.org/abs/2204.00790v2.
5	JAGADESH T , RANI B . Modeling target detection and performance analysis of electronic countermeasures for phased radar[J]. Intelligent Automation & Soft Computing, 2022, 35 (1): 449- 463.
6	邵春生. 相控阵雷达研究现状与发展趋势[J]. 现代雷达, 2016, 38 (6): 1- 4.
	SHAO C S . Study status and development trend of phased array radar[J]. Modern Radar, 2016, 38 (6): 1- 4.
7	窦晓杰, 王小平, 廖善良. 基于ADC方法的通信装备保障效能评估[J]. 火力与指挥控制, 2016, 41 (7): 110- 113.
	DOU X J , WANG X P , LIAO S L . Research on effectiveness evaluation model of communication equipment support system based on ADC method[J]. Fire Control & Command Control, 2016, 41 (7): 110- 113.
8	王强, 周怀军. 基于AHP算法的相控阵雷达系统效能评估[J]. 舰船电子对抗, 2009, 32 (3): 81- 85.
	WANG Q , ZHOU H J . Efficiency evaluation based on AHP algorithm for phased array radar system[J]. Shipboard Electronic Counter Measure, 2009, 32 (3): 81- 85.
9	王振全, 欧阳中辉. 基于模糊综合评判的雷达抗干扰效能评估研究[J]. 舰船电子工程, 2010, 30 (3): 106-108, 128.
	WANG Z Q , OUYANG Z H . Efficiency evaluation of radar eccm based on fuzzy integrated estimation[J]. Ship Electronic Engineering, 2010, 30 (2): 106-108, 128.
10	何胜杰, 郭强, 王兴虎, 等. 基于ADC分析法优化的无人机效能评估方法[J]. 无人系统技术, 2022, 5 (2): 106- 116.
	HE S J , GUO Q , WANG X H , et al. UAV performance eva-luation method optimized based on ADC analysis method[J]. Unmanned Systems Technology, 2022, 5 (2): 106- 116.
11	林志强, 樊斌斌, 王磊. 基于模糊聚类分析的相控阵雷达效能评估[J]. 电子信息对抗技术, 2020, 35 (3): 64- 67.
	LIN Z Q , FAN B B , WANG L . Effectiveness evaluation of phased array radar based on fuzzy clustering analysis[J]. Electronic Information Warfare Technology, 2020, 35 (3): 64- 67.
12	项磊, 杨新, 张扬, 等. 基于层次分析法与模糊理论的卫星效能评估[J]. 计算机仿真, 2013, 30 (2): 55- 61.
	XIANG L , YANG X , ZHANG Y , et al. Effectiveness evaluation for satellite system based on analytic hierarchy process and fuzzy theory[J]. Computer Simulation, 2013, 30 (2): 55- 61.
13	王帅杰, 何俊, 王斌, 等. 基于改进模糊层次分析法的相控阵雷达效能评估[J]. 火力与指挥控制, 2015, 40 (2): 90- 93.
	WANG S J , HE J , WANG B , et al. An effectiveness evaluation research of phased array radar based on improved fuzzy AHP[J]. Fire Control & Command Control, 2015, 40 (2): 90- 93.
14	LIU H C , LIU L , LIN Q L . Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology[J]. IEEE Trans.on Reliability, 2013, 62 (1): 23- 36. doi: 10.1109/TR.2013.2241251
15	XIAO F Y . An improved method for combining conflicting evidences based on the similarity measure and belief function entropy[J]. International Journal of Fuzzy Systems, 2018, 20 (4): 1256- 1266. doi: 10.1007/s40815-017-0436-5
16	XIAO F Y . A novel evidence theory and fuzzy preference approach-based multi-sensor data fusion technique for fault diagnosis[J]. Sensors, 2017, 17 (11): 2504- 2524. doi: 10.3390/s17112504
17	DU Y X , LU X , SU X Y , et al. New failure mode and effects analysis: an evidential downscaling method[J]. Quality and Reliability Engineering International, 2016, 32 (2): 737- 746. doi: 10.1002/qre.1753
18	CHENG G , CHEN X H , SHAN X L , et al. A new method of gear fault diagnosis in strong noise based on multi-sensor information fusion[J]. Journal of Vibration and Control, 2016, 22 (6): 1504- 1515. doi: 10.1177/1077546314542187
19	DENG X Y , DENG Y . D-AHP method with different credibility of information[J]. Soft Computing, 2019, 23 (2): 683- 691. doi: 10.1007/s00500-017-2993-9
20	YAGER R R , ALAJLAN N . Dempster-Shafer belief structures for decision making under uncertainty[J]. Knowledge-Based Systems, 2015, 80, 58- 66. doi: 10.1016/j.knosys.2014.12.031
21	DENG X Y , JIANG W . An evidential axiomatic design approach for decision making using the evaluation of belief structure satisfaction to uncertain target values[J]. International Journal of Intelligent Systems, 2018, 33 (1): 15- 32. doi: 10.1002/int.21929
22	KABIR G , TESFAMARIAM S , FRANCISQUE A , et al. Evaluating risk of water mains failure using a Bayesian belief network model[J]. European Journal of Operational Research, 2015, 240 (1): 220- 234. doi: 10.1016/j.ejor.2014.06.033
23	ZHENG X L , DENG Y . Dependence assessment in human reliability analysis based on evidence credibility decay model and IOWA operator[J]. Annals of Nuclear Energy, 2018, 112, 673- 684. doi: 10.1016/j.anucene.2017.10.045
24	FEI L G , DENG Y , HU Y . DS-VIKOR: a new multi-criteria decision-making method for supplier selection[J]. International Journal of Fuzzy Systems, 2019, 21 (1): 157- 175. doi: 10.1007/s40815-018-0543-y
25	MA J B , LIU W R , MILLER P , et al. An evidential fusion approach for gender profiling[J]. Information Sciences, 2016, 333, 10- 20. doi: 10.1016/j.ins.2015.11.011
26	石福丽, 朱一凡, 李超, 等. 军事通信网络效能评估中的多数据源融合方法[J]. 火力与指挥控制, 2012, 37 (7): 18- 23.
	SHI F L , ZHU Y F , LI C , et al. Multiple data sources fusion method for effectiveness evaluation of military communication network[J]. Fire Control & Command Control, 2012, 37 (7): 18- 23.
27	杨米, 陈建忠, 牛英滔. 基于云模型与证据理论的通信电子防御效能评估[J]. 计算机工程, 2017, 43 (6): 40-45, 52.
	YANG M , CHEN J Z , NIU Y T . Evaluation of communication electronic defense effectiveness based on cloud model and evidence theory[J]. Computer Engineering, 2017, 43 (6): 40-45, 52.
28	杨文平. 基于DS-GRA机载雷达组网资源管理效能评估[J]. 指挥控制与仿真, 2018, 40 (1): 80- 85.
	YANG W P . Effectiveness evaluation of airborne netted radar resource management based on DS-GRA[J]. Command Control & Simulation, 2018, 40 (1): 80- 85.
29	DI P , WANG X , CHEN T , et al. Multisensor data fusion in testability evaluation of equipment[J]. Mathematical Problems in Engineering, 2020, 2020 (43): 1- 16.
30	DENG Y. Deng entropy: a generalized Shannon entropy to measure uncertainty[EB/OL]. [2022-10-20]. http://rxiv.org/abs/1502.0222.
31	SINGH P , DHIMAN G . Uncertainty representation using fuzzy-entropy approach: special application in remotely sensed high-resolution satellite images (RSHRSIs)[J]. Applied Soft Computing, 2018, 72, 121- 139. doi: 10.1016/j.asoc.2018.07.038
32	王旋, 狄鹏, 尹东亮. 基于Lance距离和信度熵的冲突证据融合方法[J]. 系统工程与电子技术, 2022, 44 (2): 592- 602.
	WANG X , DI P , YIN D L . Conflict evidence fusion method based on Lance distance and credibility entropy[J]. Systems Engineering and Electronics, 2022, 44 (4): 592- 602.
33	DEMPSTER A P . Upper and lower probabilities induced by a multivalued mapping[J]. The Annals of Mathematical Statistics, 1967, 38 (2): 325- 339. doi: 10.1214/aoms/1177698950

指标	权重值	评语集
指标	权重值	θ₁	θ₂	θ₃	θ₄	θ₅
U₁₁	0.309	0.15	0.31	0.42	0.12	0.00
U₁₂	0.433	0.18	0.63	0.15	0.04	0.00
U₁₃	0.258	0.13	0.44	0.27	0.16	0.00
U₂₁	0.094	0.15	0.35	0.29	0.11	0.10
U₂₂	0.212	0.07	0.46	0.41	0.06	0.00
U₂₃	0.255	0.12	0.32	0.38	0.18	0.00
U₂₄	0.306	0.05	0.23	0.33	0.34	0.05
U₂₅	0.133	0.0	0.24	0.22	0.54	0.00
U₃₁	0.373	0.15	0.36	0.28	0.21	0.00
U₃₂	0.123	0.12	0.22	0.28	0.29	0.09
U₃₃	0.148	0.00	0.11	0.34	0.48	0.07
U₃₄	0.207	0.07	0.36	0.31	0.22	0.04
U₃₅	0.149	0.08	0.37	0.33	0.16	0.06
U₄₁	0.211	0.04	0.27	0.51	0.18	0.00
U₄₂	0.427	0.16	0.39	0.10	0.35	0.00
U₄₃	0.362	0.04	0.67	0.14	0.13	0.02
U₅₁	0.534	0.30	0.26	0.21	0.15	0.08
U₅₂	0.466	0.19	0.47	0.18	0.12	0.04

对象	评语集
对象	优秀	良好	中等	一般	差
预警探测能力U₁	0.157 83	0.482 10	0.264 39	0.095 68	0
情报处理能力U₂	0.074 84	0.314 32	0.341 32	0.244 82	0.024 70
指挥控制能力U₃	0.097 12	0.307 27	0.302 54	0.254 42	0.038 65
战场生存能力U₄	0.091 24	0.466 04	0.200 99	0.234 49	0.007 24
系统可靠性U₅	0.248 74	0.357 86	0.196 02	0.136 02	0.061 36

最终融合结果	方法1	方法2	方法3	方法4
m(θ₁)	0.002 9	0.003 9	0.004 7	0.004 1
m(θ₂)	0.857 3	0.856 5	0.866 9	0.874 1
m(θ₃)	0.118 8	0.114 7	0.105 9	0.100 8
m(θ₄)	0.021 0	0.024 9	0.022 5	0.021 0
m(θ₅)	0	0	0	0

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