数据立方体关联挖掘在红外抗干扰中的应用

吴鑫; 伍友利; 牛得清; 蔡宇轩; 徐洋; 陈鞭

doi:10.13700/j.bh.1001-5965.2021.0096

数据立方体关联挖掘在红外抗干扰中的应用

doi: 10.13700/j.bh.1001-5965.2021.0096

吴鑫¹,
伍友利^1, ,,
牛得清¹,
蔡宇轩¹,
徐洋²,
陈鞭¹

1.
空军工程大学航空工程学院, 西安 710038
2.
中国空气动力研究与发展中心, 绵阳 621000

基金项目:

国家自然科学基金 61973253

详细信息

通讯作者:
伍友利, E-mail: 18359943663@163.com

中图分类号: TN216
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- 文章访问数: 244
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- PDF下载量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2021-03-01
- 录用日期: 2021-06-01
- 网络出版日期: 2021-07-06
- 整期出版日期: 2022-11-20

Application of data cube association mining in infrared anti-jamming

1.
School of Aeronautical Engineering, Air Force Engineering University, Xi'an 710038, China
2.
China Aerodynamics Research and Development Center, Mianyang 621000, China

Funds:

National Natural Science Foundation of China 61973253

More Information

Corresponding author: WU Youli, E-mail: 18359943663@163.com

摘要

摘要:
为了分析空空导弹在红外抗干扰过程中, 各干扰量与脱靶量之间的耦合关系, 利用红外抗干扰试验仿真平台, 通过设置干扰量获得海量数据, 构建数据立方体对海量数据进行数据预处理, 同时统计出数据集中各事务的支持度计数, 将处理后的数据利用FP-Growth关联规则算法挖掘干扰量与脱靶量之间的关联规则。通过对关联规则的分析得到：导弹进入角与弹目距离是影响导弹脱靶量大小的主要因素, 各个干扰量之间不能随意组合, 应该根据导弹来袭方向、目标机动方式选择诱饵投掷策略, 否则干扰效果会大大下降。同时设置一组对比试验, 验证基于数据立方体的FP-Growth算法处理高维数的脱靶量数据, 拥有较高的挖掘效率。
- 数据立方体 /
- 数据处理 /
- FP-Growth算法 /
- 关联规则 /
- 红外抗干扰
Abstract:
This study aims at analyzing the coupling relationship between disturbance variables and miss distance of air-to-air missiles in infrared anti-jamming. Massive data are obtained by setting disturbance variables through an experimental simulation platform. The data cube is then constructed to preprocess the massive data and compute the support count of each transaction. The processed data are used to mine the association rules between disturbance variables and miss distance by FP-Growth algorithm. Analysis of the association rules show that the entry angle of the missile and the distance between the missile and the target are the main factors for miss distance. The disturbance variables cannot be combined freely, but should be selected according to the attack direction of the missile and the target maneuver mode. Otherwise, the jamming effect will be greatly reduced. A group of comparative experiments is also set up to verify the high efficiency of FP-Growth algorithm based on the data cube in processing the miss distance data with a high dimension.
- data cube /
- data processing /
- FP-Growth algorithm /
- association rule /
- infrared anti-jamming

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图 1 红外对抗仿真流程

Figure 1. Simulation process of infrared countermeasure model

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图 2 四维数据立方体

Figure 2. Four-dimensional data cube

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图 3 按MD降维后数据立方体示意图

Figure 3. Diagram of data cube after dimension reduction by MD

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图 4 参数频数统计及数据中参数重新排序

Figure 4. Parameter frequency statistics and parameter reordering in each piece of data

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图 5 读入数据生成FP树

Figure 5. Data reading to generate FP tree

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图 6 各末节点对应的条件FP树

Figure 6. Conditional FP tree for each final node

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表 1 干扰量参数设定范围

Table 1. Range of disturbance variable parameters

干扰量	参数	取值设定
诱饵投掷参数	投掷总数N_s/个	12, 24, 36, 48
	每次投掷个数N_e/个	1, 2
	每组投掷个数N_f/个	4, 6, 12
	投掷时机T_l/s	0.5, 1, 1.5, 2, 2.5
	投掷组内间隔T_i/s	0.1, 0.2, 0.3, 0.4, 0.5
	投掷组间间隔T_e/s	0.2, 0.4, 0.6, 0.8, 1.0
	投掷速度V_f/(m·s^-1)	10, 20, 30, 40
	水平投掷角度θ_l/(°)	0, 30, 60, 90, 120, 150
	垂直投掷角度θ_v/(°)	-90, -60, 0，30，60, 90
弹目相对态势	进入角θ_m/(°)	10, 20, 30, 70, 80, 90, 130, 140, 150
弹目相对态势	弹目距离D_r/km	(0, 3], (3, 4], (4, 5], (5, 6], (6, 7], (7, 8], (8, 9], (9, 10]

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表 2 MD1立方体的1-D关系

Table 2. 1-D relation table of MD1 cube

PIB	MTRP	TMM	数目
N_s1	*	*	10 656
⋮	⋮	⋮	⋮
*	θ_m1	*	5 556
⋮	⋮	⋮	⋮
*	*	1	9 878
注：*代表可取任意参数。

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表 3 MD1立方体的2-D关系

Table 3. 2-D relation table of MD1 cube

PIB	MTRP	TMM	数目
N_s1	θ_m1	*	3 078
⋮	⋮	⋮	⋮
*	θ_m1	1	2 043
⋮	⋮	⋮	⋮
N_s1	*	1	4 028
⋮	⋮	⋮	⋮
注：*代表可取任意参数。

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表 4 MD1立方体的3-D关系

Table 4. 3-D relation table of MD1 cube

PIB	MTRP	TMM	数目
N_s1	θ_m1	1	1 059
⋮	⋮	⋮	⋮
注：*代表可取任意参数。

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表 5 干扰量参量编号

Table 5. Disturbance variable numbering

干扰量	参数	取值设定
诱饵投掷参量	N_s/个	12(4), 24(5), 36(6), 48(7)
	N_e/个	1(8), 2(9)
	N_f/个	4(10), 6(11), 12(12)
	T_l/s	0.5(13), 1(14), 1.5(15), 2(16), 2.5(17)
	T_i/s	0.1(18), 0.2(19), 0.3(20), 0.4(21), 0.5(28.2)
	T_e/s	0.2(23), 0.4(24), 0.6(25), 0.8(26), 1(27)
	V_f/(m·s^-1)	10(28), 20(29), 30(30), 40(31)
	θ_l/(°)	0(32), 30(33), 60(34), 90(35), 120(36), 150(37)
	θ_v/(°)	-90(38), -60(39), 0(40), 30(41), 60(42), 90(43)
弹目相对态势	θ_m/(°)	10(44), 20(45), 30(46), 70(47), 80(48), 90(49), 130(50), 140(51), 150(52)
弹目相对态势	D_r/km	(0, 3](53), (3, 4](54), (4, 5](55), (5, 6](56), (6, 7](57), (7, 8](58), (8, 9](59), (9, 10](60)
目标机动	TMM	无机动(61), 左转弯(62), 右转弯(63), 跃升(64)
脱靶量	MD/m	MD1(1), MD1(2), MD1(3)
注: 括号内数字为编号。

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表 6 MD1部分内部关联规则(MD1 < 12 m)

Table 6. Inner association rules of MD1 (MD1 < 12 m)

序号	Conf	Sup	Kulc	IR	规则
1	0.989	0.011	0.571	0.968	13, 52, 59→23, 1
2	0.976	0.006	0.538	0.925	37, 47, 53→17, 1
3	0.972	0.007	0.549	0.901	21, 37, 53→17, 1
4	0.966	0.005	0.533	0.935	5, 43, 53→17, 1
5	0.961	0.009	0.559	0.933	9, 36, 53→17, 1

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表 7 MD2部分内部关联规则(12 m≤MD2 < 16 m)

Table 7. Inner association rules of MD2 (12 m≤MD2 < 16 m)

序号	Conf	Sup	Kulc	IR	规则
1	0.996	0.005	0.513	0.975	34, 53→61, 2
2	0.988	0.006	0.512	0.975	44, 56→8, 2
3	0.975	0.008	0.517	0.967	6, 32→8, 2

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表 8 MD3部分内部关联规则(MD3≥16 m)

Table 8. Inner association rules of MD3 (MD3≥16 m)

序号	Conf	Sup	Kulc	IR	规则
1	0.986	0.006	0.555	0.918	17, 60, 62→27, 3
2	0.977	0.006	0.706	0.588	17, 60, 62→22, 3
3	0.984	0.006	0.511	0.978	10, 13, 53→9, 3
4	0.965	0.005	0.543	0.988	13, 24, 53→10, 3

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表 9 部分方体间外联关联规则

Table 9. Part of outer association rules

序号	Conf	Sup	Kulc	IR	规则
1	0.986	0.010	0.508	0.984	17, 26, 53→1
2	0.983	0.006	0.505	0.990	52, 53, 63→1
3	0.980	0.006	0.505	0.991	32, 44, 64→1
4	0.973	0.006	0.505	0.990	11, 52, 64→1
5	0.972	0.007	0.506	0.988	37, 49, 59, 64→1
6	0.992	0.005	0.510	0.979	17, 34, 63→2
7	0.987	0.005	0.511	0.978	52, 55, 61→2
8	0.977	0.006	0.512	0.977	35, 52, 61→2
9	0.975	0.007	0.514	0.973	44, 58, 64→2
10	0.971	0.006	0.513	0.974	33, 48, 63→2
11	0.996	0.009	0.537	0.927	50, 58, 61→3
12	0.987	0.008	0.531	0.937	9, 52, 58, 62→3
13	0.978	0.009	0.537	0.927	8, 51, 58→3
14	0.974	0.009	0.534	0.932	35, 51, 58→3
15	0.972	0.007	0.527	0.946	52, 60, 63→3
16	0.967	0.009	0.537	0.927	49, 54, 64→3

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表 10 不同数据量下算法效率对比

Table 10. Comparison of algorithm efficiency with different data volumes

数据量	T_FP/s	T_{DC_FP}/s
1 000	56.4	55.9
5 000	534.6	108.7
10 000	5 063.7	305.3
15 000	9 886.2	502.9
20 000	20 043.2	745.8
50 000	*	1 034.9
注：*代表数据量过大，结果未获得。

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