基于多传感器的雷达对抗侦察数据融合算法

doi:10.12305/j.issn.1001-506X.2023.01.13

摘要/Abstract

摘要：

为提升复杂电磁环境下雷达对抗侦察系统的可靠性及抗干扰能力, 提出了一种基于多传感器融合的综合权值估计算法。首先由各传感器对原始数据进行分批估计得到局部期望及方差, 然后利用四分位离散度剔除异常数据, 接着针对局部估计结果通过测度算子以及自适应融合估计算法分别确定各传感器权重, 最后利用综合权重进行加权得到融合结果。仿真结果表明, 所提算法在不同情况下均能得到较高的融合精度, 较现有算法具有更好的适应能力及准确性。

关键词: 数据融合, 雷达对抗侦察, 一致性测度, 自适应加权

Abstract:

To improve the reliability and anti-jamming capability of radar countermeasures and reconnaissance systems in complex electromagnetic environments, a comprehensive weight estimation algorithm based on multi-sensor fusion is proposed. Firstly, the original data is estimated by each sensor in batches to obtain the local expectations and variances. Secondly, the quartile dispersion is used to eliminate the abnormal data. Thirdly, the weight of each sensor is ascertained by the measurement operator and the adaptive fusion estimation algorithm for the local estimation results. Finally, the comprehensive weight is used for weighting to obtain the fusion result. Simulation results show that the proposed algorithm can obtain higher fusion accuracy under different conditions, and has better adaptability and accuracy than existing algorithms.

Key words: data fusion, radar countermeasure and reconnaissance, consistency measure, adaptive weighting

中图分类号:

TP274

刘康, 何明浩, 韩俊, 冯明月, 都兴霖. 基于多传感器的雷达对抗侦察数据融合算法[J]. 系统工程与电子技术, 2023, 45(1): 101-107.

Kang LIU, Minghao HE, Jun HAN, Mingyue FENG, Xinglin DU. Data fusion algorithm for radar countermeasures and reconnaissance based on multi-sensor[J]. Systems Engineering and Electronics, 2023, 45(1): 101-107.

图/表 17

图1

表1

表2

图2

表3

表4

表5

表6

图3

表7

表8

表9

图4

表10

表11

表12

图5

参考文献 21

1	滕小虎. 雷达电子对抗技术及其运用研究[J]. 数字技术与应用, 2019, 37 (5): 102- 103.
	TENG X H . Research on radar electronic countermeasure technology and its application[J]. Digital Technology and Application, 2019, 37 (5): 102- 103.
2	LI M. Design of radar countermeasure reconnaissance and interference system[C]//Proc. of the 6th International Conference on Measurement, Instrumentation and Automation, 2017.
3	MI X J , LYU T X , TIAN Y , et al. Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system[J]. ISA Transactions, 2021, 112, 137- 149. doi: 10.1016/j.isatra.2020.12.009
4	WANG J Y , YU Q Z . A dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator[J]. Applied Intelligence, 2020, 50 (11): 3837- 3851. doi: 10.1007/s10489-020-01739-8
5	田威, 黄高明. 非理想关联下多传感器系统误差的稳健估计[J]. 电子与信息学报, 2018, 40 (3): 641- 647.
	TIAN W , HUANG G M . Robust estimation of multi-sensor system error under non-ideal correlation[J]. Journal of Electronics & Information Technology, 2018, 40 (3): 641- 647.
6	朱垲, 宋欣, 何建祥, 等. 基于小波降噪和自适应加权法的温室数据融合[J]. 江苏农业科学, 2021, 49 (5): 180- 186.
	ZHU K , SONG X , HE J X , et al. Greenhouse data fusion based on wavelet denoising and adaptive weighting method[J]. Jiangsu Agricultural Sciences, 2021, 49 (5): 180- 186.
7	戴海发, 卞鸿巍, 王荣颖, 等. 一种改进的多传感器数据自适应融合方法[J]. 武汉大学学报(信息科学版), 2020, 45 (10): 1602- 1609.
	DAI H F , BIAN H W , WANG R Y , et al. An improved multi-sensor data adaptive fusion method[J]. Journal of Wuhan University (Information Science Edition), 2020, 45 (10): 1602- 1609.
8	GELB A . Applied optimal estimation[M]. Cambridge: MIT Press, 2002.
9	DING W D , WANG J L , RIZOS C , et al. Improving adaptive Kalman estimation in GPS/INS integration[J]. Journal of Navigation, 2007, 60 (3): 517- 529. doi: 10.1017/S0373463307004316
10	LUO R C , YIH C C , SU K L . Multisensor fusion and integration: approaches, applications, and future research directions[J]. IEEE Sensors Journal, 2002, 2 (2): 107- 119. doi: 10.1109/JSEN.2002.1000251
11	WU S L , BI Y X , ZENG X Q , et al. Assigning appropriate weights for the linear combination data fusion method in information retrieval[J]. Information Processing and Management, 2009, 45 (4): 413- 426. doi: 10.1016/j.ipm.2009.02.003
12	敬如雪, 高玉琢. 基于多传感器的数据融合算法研究[J]. 现代电子技术, 2020, 43 (10): 10- 13.
	JING R X , GAO Y Z . Research on data fusion algorithm based on multi-sensor[J]. Modern Electronic Technology, 2020, 43 (10): 10- 13.
13	王华, 邓军, 王连华, 等. 改进的一致性数据融合算法及其应用[J]. 中国矿业大学学报, 2009, 38 (4): 590- 594.
	WANG H , DENG J , WANG L H , et al. Improved consistent data fusion algorithm and its application[J]. Journal of China University of Mining & Technology, 2009, 38 (4): 590- 594.
14	YU Y J , LIU X Q , XU C N , et al. Multi-sensor data fusion algorithm based on the improved weighting factor[J]. Journal of Physics: Conference Series, 2021, 1754 (1): 012227.
15	张军, 杨子晨. 多传感器数据采集系统中的数据融合研究[J]. 传感器与微系统, 2014, 33 (3): 52- 54.52-54, 57
	ZHANG J , YANG Z C . Research on data fusion in multi-sensor data acquisition system[J]. Sensors and Microsystems, 2014, 33 (3): 52- 54.52-54, 57
16	FAN W L, MA X M. Dielectric loss angle data processing based on adaptive weighted data fusion algorithm of the aging mine cable[C]//Proc. of the 29th Chinese Control and Decision Conference, 2017: 5739-5742.
17	罗中良, 张前进, 方清城, 等. 智能仪表中的一种数据融合方法[J]. 仪表技术与传感器, 2002, 3, 45- 46.
	LUO Z L , ZHANG Q J , FANG Q C , et al. A data fusion method in smart meters[J]. Instrument Technology and Sensors, 2002, 3, 45- 46.
18	GAO S S , ZHONG Y M , LI W . Random weighting method for multisensor data fusion[J]. IEEE Sensors Journal, 2011, 11 (9): 1955- 1961.
19	程曦, 周国正, 唐西明, 等. 基于涡流技术的燃料棒氧化膜测量信号有效性评估与统计[J]. 核动力工程, 2020, 41 (1): 49- 53.
	CHENG X , ZHOU G Z , TANG X M , et al. Evaluation and statistics of the measurement signal validity of fuel rod oxide film based on eddy current technology[J]. Nuclear Power Engineering, 2020, 41 (1): 49- 53.
20	杨锡运, 张璜, 关文渊, 等. 基于滑动分块百分位数Bootstrap法的风电功率概率区间预测[J]. 太阳能学报, 2019, 40 (2): 430- 437.
	YANG X Y , ZHANG H , GUAN W Y , et al. Wind power probability interval prediction based on sliding block percentile Bootstrap method[J]. Acta Solar Energy, 2019, 40 (2): 430- 437.
21	司刚全, 张寅松, 娄勇. 考虑自支持度和互支持度的多传感器一致性测度算子[J]. 西安交通大学学报, 2012, 46 (10): 20- 23.20-23, 35
	SI G Q , ZHANG Y S , LOU Y . Multi-sensor consistency measure operator considering self-support and mutual support[J]. Journal of Xi'an Jiaotong University, 2012, 46 (10): 20- 23.20-23, 35

期望及方差	传感器
期望及方差	1	2	3	4	5	6
$\hat{x}_i$	3 001.202	3 001.689	2 997.329	2 997.038	3 003.027	2 996.157
$\hat{\sigma}_i$	0.011	0.362	0.499	0.714	0.846	1.470

融合结果及误差	算法
融合结果及误差	本文方法	自适应加权	贝叶斯	算术平均	文献[12]	文献[13]
$\hat{x}$	3 000.400	3 001.200	3 001.200	2 999.407	3 001.200	3 001.435
绝对误差	0.400	1.200	1.200	0.593	1.200	1.435

耗时及误差	算法
耗时及误差	本文方法	自适应加权	贝叶斯	算术平均	文献[12]	文献[13]
平均耗时/s	＜0.01	＜0.01	＜0.01	＜0.01	0.44	8.156
平均误差/MHz	0.585 5	0.633 5	0.635 0	0.922 3	0.700 7	2.729 5

期望及方差	传感器
期望及方差	1	2	3	4	5	6
$\hat{x}_i$	3 000.576	3 001.730	2 997.855	2 997.641	3 003.539	3 039.218
$\hat{\sigma}_i$	0.225	1.025	1.053	1.349	1.394	1.457

融合结果及误差	算法
融合结果及误差	本文方法	自适应加权	贝叶斯	算术平均	文献[12]	文献[13]
$\hat{x}$	3 000.557	3 001.300	3 001.301	3 006.760	3 000.411	2 997.863
绝对误差	0.557	1.300	1.301	6.760	0.411	2.137