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

Abstract

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

CLC Number:

TP274

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.

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References 21

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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

Data fusion algorithm for radar countermeasures and reconnaissance based on multi-sensor

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期望及方差	传感器
期望及方差	1	2	3	4	5	6
$\hat{x}_i$	2 999.154	2 998.105	3 002.208	2 997.772	3 002.590	2 996.696
$\hat{\sigma}_i$	1.437	1.241	0.270	0.860	1.141	1.317

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]
$\hat{x}$	3 000.449	3 001.463	3 001.442	2 999.421	3 001.463	2 996.618
绝对误差	0.449	1.463	1.442	0.579	1.463	3.382

期望及方差	传感器
期望及方差	1	2	3	4	5	6
$\hat{x}_i$	3 000.647	2 998.580	3 002.647	2 997.263	3 003.875	3 039.373
$\hat{\sigma}_i$	1.601	1.266	0.662	0.702	1.166	1.481

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]
$\hat{x}$	3 000.515	3 003.169	3 003.172	3 007.064	3 001.047	3 000.665
绝对误差	0.515	3.169	3.172	7.064	1.047	0.665