基于关注势的战场态势热力图构建方法

doi:10.3969/j.issn.1001-506X.2021.01.15

Abstract

Abstract:

In the face of highly complex and changeable modern battlefield, how to quickly and intuitively find the situation hot spots on the battlefield and reduce the cognitive load is a great challenge for commanders. Aiming at the problem of finding hot spots in battlefield situation under time-varying conditions, on the basis of analyzing the forming mechanism of hot spots, the theory of attention potential is put forward, and the improved logistic time decay function (ILTDF) in line with human cognitive characteristics is constructed. Then the weighted kernel density estimation decay over time (W-KDE-DOT) and the construction method of the heat map of the battlefield situation based on attention potential are proposed. The experimental results show that the heat map construction method based on the theory of attention potential can depict the time-varying characteristics of the situation hot spots more accurately. And the proposed method is more consistent with the objective law of battlefield situation hot spot discovery, and can provide support for commanders and decision-makers to accurately grasp the battlefield situation hot spot.

Key words: situation awareness, attention potential, heat map, weighted kernel density estimation (W-KDE) method, time decay function

CLC Number:

E917

Haoyang DONG, Dongge ZHANG, Ning QI, Minggang YU, Yanjie NIU. Construction method of battlefield situation heat map based on attention trends[J]. Systems Engineering and Electronics, 2021, 43(1): 121-129.

Figures/Tables 13

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References 30

1	DONG J , WU G W , YANG T T , et al. Battlefield situation awareness and networking based on agent distributed computing[J]. Physical Communication, 2019, 33, 178- 186.
2	BISHT S , BHARATI H S , TANEJA S B , et al. Command agent belief architecture to support commander decision making in military simulation[J]. Defence Science Journal, 2017, 68 (1): 46- 53.
3	赵慧赟,张东戈.战场态势感知研究综述[C]//第三届中国指挥控制大会, 2015: 86-91.
	ZHAO H Y, ZHANG D G. Review of battlefield situation awareness research[C]//Proc.of the 3rd China Command and Control Conference, 2015: 86-91.
4	EUGENE S J, HIEN N, JACOB R, et al. A framework for dynamic context-centric commander decision support[C]//Proc.of the Human Factors and Ergonomics Society Annual Meeting, 2016, 218-222.
5	ENDSLEY M R. Design and evaluation for situation awareness enhancement[C]//Proc.of the 32nd Human Factors and Ergonomics Society Annual Meeting, 1998.
6	BASS T. Multisensor data fusion for next generation distributed intrusion detection systems[C]//Porc.of the IRIS National Symposium on Sensor and Data Fusion, 1999: 24-27.
7	WEBB J , AHMAD A , MAYNARD S B , et al. A situation awareness model for information security risk management[J]. Computers & Security, 2014, 44, 1- 15.
8	LOPEZ L B , CARAGUAY A V , MAESTRE V J M , et al. Towards incidence management in 5G based on situational awareness[J]. Future Internet, 2017, 9 (1): 3- 16.
9	FRANCESCO D M , ALESSANDRO B , ENRICO Z , et al. An approach based on support vector machines and a K-D tree search algorithm for identification of the failure domain and safest operating conditions in nuclear systems[J]. Progress in Nuclear Energy, 2016, 88, 297- 309.
10	黄亚锋, 李旭东, 张航峰. 战场态势多尺度表达研究[J]. 系统仿真学报, 2018, 30 (2): 452- 458, 464.
	HUANG Y F , LI X D , ZHANG H F . Multi-scale representation of battlefield situation[J]. Journal of System Simulation, 2018, 30 (2): 452- 458, 464.
11	李尧, 耿伯英, 李启元. 基于态势熵的战场情报融合系统建模与仿真[J]. 火力与指挥控制, 2019, 44 (12): 56- 60.
	LI Y , GENG B Y , LI Q Y . Modeling and simulation of battlefield intelligence fusion system based on situation entropy[J]. Fire Control & Command Control, 2019, 44 (12): 56- 60.
12	于淼, 杜正军. 基于作战势能比值的态势评估方法[J]. 军事运筹与系统工程, 2012, 26 (3): 23- 26.
	YU M , DU Z J . Situation evaluation method based on the ratio of combat potential energy[J]. Military Operations Research and Systems Engineering, 2012, 26 (3): 23- 26.
13	孙雅薇, 周超. 态势图中几种"势"的表征方法[J]. 火力与指挥控制, 2018, 43 (4): 79- 83.
	SUN Y W , ZHOU C . Characterization method of several potential in situation picture[J]. Fire Control & Command Control, 2018, 43 (4): 79- 83.
14	董浩洋, 张东戈, 万贻平, 等. 战场态势热力图构建方法研究[J]. 指挥控制与仿真, 2017, 39 (5): 1- 8.
	DONG H Y , ZHANG D G , WAN Y P , et al. Research on construction method of heat map for battlefield situation[J]. Command Control & Simulation, 2017, 39 (5): 1- 8.
15	GATRELL A C , BAILEY T C . Interactive spatial data analysis in medical geography[J]. Social Science & Medicine, 1996, 42 (6): 843- 855.
16	PAMPHILE T R , LLUÍS J , JEAN-CHRISTOPHE J , et al. Versatile sequential sampling algorithm using kernel density estimation[J]. European Journal of Operational Research, 2020, 284 (1): 201- 211.
17	WILSON C M , GERARD P . Kernel density estimation for hierarchical data[J]. Communications in Statistics-Theory and Methods, 2020, 49 (6): 1495- 1512.
18	PRYKE A, MOSTAGHIM S, NAZEMI A. Heatmap visualisation of population based multi objective algorithms[C]//Proc.of the International Conference on Evolutionary Multi-Criterion Optimization, 2007: 361-375.
19	TUSAR T , FILIPIC B . Visualization of pareto front approximations in evolutionary multiobjective optimization: a critical review and the prosection method[J]. IEEE Trans.on Evolutionary Computation, 2015, 19 (2): 225- 245.
20	BANDARU S , NG A H C , DEB K . Data mining methods for knowledge discovery in multi-objective optimization: Part A-survey[J]. Expert Systems with Application, 2017, 70, 139- 159.
21	HERVELLA L S , JOSÉ R , NOVO J , et al. Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images[J]. Computer Methods and Programs in Biomedicine, 2019, 186, 105201.
22	HU Y , SCARROTT C . Evmix: an R package for extreme value mixture modeling, threshold estimation and boundary corrected kernel density estimation[J]. Journal of Statistical Software, 2018, 84 (5): 1- 27.
23	WANG X , TSOKOS C P , SAGHAFI A . Improved parameter estimation of time dependent kernel density by using artificial neural networks[J]. Journal of Finance & Data Science, 2018, 4 (3): 172- 182.
24	SHEATHER S J, JONES M C. A reliable data-based bandwidth selection method for kernel density estimation, 1991, 53(3): 683-690.
25	LEE S , PALMO K , KRIMM S . New out-of-plane angle and bond angle internal coordinates and related potential energy functions for molecular mechanics and dynamics simulations[J]. Journal of Computational Chemistry, 2015, 20 (10): 1067- 1084.
26	YANG D , HE Y , WU B , et al. Drinking water and sanitation conditions are associated with the risk of malaria among children under five years old in sub-Saharan Africa: a Logistic regression model analysis of national survey data[J]. Journal of Advanced Research, 2020, 21 (1): 1- 13.
27	RIVERO F , JARA J C . Asymptotic behaviour for prey-predator systems and logistic equations with unbounded time-dependent coefficients[J]. Discrete & Continuous Dynamical Systems-Series A, 2017, 34 (10): 4127- 4137.
28	CHEN W , SHAHABI H , SHIRZADI A , et al. A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment[J]. Geocarto International, 2018, 33 (12): 1398- 1420.
29	WANG H Y , ZHU R , MA P . Optimal subsampling for large sample Logistic regression[J]. Journal of the American Statistical Association, 2018, 113 (522): 829- 844.
30	KANG J , REICH B J , STAICU A M . Scalar-on-image regression via the soft-thresholded gaussian process[J]. Biometrika, 2018, 105 (1): 165- 184.

组	目标	坐标	各时间步通信频次(次/10 s)
组	目标	坐标	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20
1	a	(320, 80)	14.3	14.3	14.3	14.3	14.3	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5
	b	(329, 136)	14.3	14.3	14.3	14.3	14.3	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5
	c	(280, 113)	14.3	14.3	14.3	14.3	14.3	5	5	5	5	5	5	5	5	5	5	5	5	5	5	5
2	d	(100, 119)	14.6	14.3	14.6	14.6	13.9	14.6	14.3	14.6	14.6	13.9	14.6	14.3	14.6	14.6	13.9	14.6	14.3	14.6	14.6	14.6
2	e	(50, 143)	14.6	14.6	14.5	14.6	14.4	14.6	14.6	14.5	14.6	14.4	14.6	14.6	14.5	14.6	14.4	14.6	14.6	14.6	14.6	14.6
3	f	(66, 307)	14	14	14	5.5	5.5	5.6	5.2	5.5	5.3	5.5	5.6	5.5	5.6	5.5	14	14	14	5.5	5.6	5.5
	g	(88, 350)	14	14	14	5.5	5.5	5.4	5.5	5.6	5.6	5.5	5.4	5.5	5.4	5.1	14	14	14	5.5	5.6	5.5
	h	(36, 336)	14	14	14	5.5	5.4	5.7	5.5	5.4	5.4	5.7	5.4	5.5	5.4	5.7	14	14	14	5.5	5.4	5.5
其他目标			5	5.2	5.6	5.1	5.0	5.2	5	5.6	5.1	5.0	5.2	5.1	5.0	5.6	5.1	5.0	5.2	5.1	5.0	5.2

组	目标	坐标	各时间步影响因子函数值
组	目标	坐标	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20
1	a	(320, 80)	14.3	14.3	14.3	14.3	14.3	14.0	14.0	13.9	13.9	13.8	13.6	13.2	12.6	11.7	10.2	8.4	5.0	5.0	5.0	5.0
	b	(329, 136)	14.3	14.3	14.3	14.3	14.3	14.0	14.0	13.9	13.9	13.8	13.6	13.2	12.6	11.7	10.2	8.4	5.0	5.0	5.0	5.0
	c	(280, 113)	14.3	14.3	14.3	14.3	14.3	14.0	14.0	13.9	13.9	13.8	13.6	13.2	12.6	11.7	10.2	8.4	5.0	5.0	5.0	5.0
2	d	(100, 119)	14.6	14.3	14.6	14.6	13.9	14.6	14.3	14.6	14.6	13.9	14.6	14.3	14.6	14.6	13.9	14.6	14.3	14.6	14.6	14.6
2	e	(50, 143)	14.6	14.6	14.5	14.6	14.4	14.6	14.6	14.5	14.6	14.4	14.6	14.6	14.5	14.6	14.4	14.6	14.6	14.6	14.6	14.6
3	f	(66, 307)	14.0	14.0	14.0	14.0	14.0	13.9	13.9	13.8	13.6	13.2	12.6	11.7	10.2	8.4	14.0	14.0	14.0	14.0	14.0	13.9
	g	(88, 350)	14.0	14.0	14.0	14.0	14.0	13.9	13.9	13.8	13.6	13.2	12.6	11.7	10.2	8.4	14.0	14.0	14.0	14.0	14.0	13.9
	h	(36, 336)	14.0	14.0	14.0	14.0	14.0	13.9	13.9	13.8	13.6	13.2	12.6	11.7	10.2	8.4	14.0	14.0	14.0	14.0	14.0	13.9

[1]	Zilong WU, Hong CHEN, Yingke LEI. Visualization research on improved DenseNet applied to recognize a radio station's link establishment behavior [J]. Systems Engineering and Electronics, 2021, 43(5): 1371-1381.
[2]	ZHANG Dong-ge1, MENG Hui2,3, ZHAO Hui-yun1. Analytical measurement model of situation awareness [J]. Systems Engineering and Electronics, 2016, 38(8): 1808-1815.
[3]	MENG Hui, ZHANG Dong-ge. Model of situation awareness velocity under continuous changing situation [J]. Journal of Systems Engineering and Electronics, 2009, 31(12): 2909-2913.

Construction method of battlefield situation heat map based on attention trends

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