卫星与雷达位置数据自适应关联

doi:10.3969/j.issn.1001-506X.2021.01.12

摘要/Abstract

摘要：

卫星与雷达间的数据关联能够实现海上预警探测过程中由大范围预警向精细跟踪过渡转换,但传统关联模型关联速度慢且难以适应舰船编队目标的非刚性变换、虚警漏报等情形。对此，提出一种卫星与雷达位置数据自适应关联模型。首先，采用多层神经网络提取卫星数据和雷达数据的整体差异参数。然后，将参数通过位移变换估计网络实现两类信源目标的匹配,解决不同信源间的时间间隔和定位误差导致的空间位置差异。最后，对匹配后的目标进行关联判决。仿真实验结果表明,该模型关联速度快,精度高,能够实时处理大规模多源数据关联任务。同时在应对非刚性变换、定位误差、虚警漏报等场景下有较好的鲁棒性。

关键词: 多源数据关联, 舰船编队目标, 神经网络, 自适应模型, 空间位置误差

Abstract:

The data association between satellite and radar can realize the transition from large-scale early warning to fine tracking in the process of the early warning detection. However, the traditional association model has a slow association speed and is difficult to deal with the non-rigid transformation, false alarm and missing detection of ship formation targets. In this regard, an adaptive association model is presented for satellite and radar position data. Firstly, the multi-layer network (MLN) is used to extract the global difference parameters of the satellite data and the radar data. Then, in order to solve the spatial position error caused by time interval and positioning error, the parameters are put into the network of displacement transformation estimation to match the targets from the two types of sources. Finally, an association judgment is accomplished for the matched targets. Simulation and experimental results show that the proposed model achieves good performance both on speed and accuracy, and can handle the large-scale multi-source data association in real-time application. Besides, the model has good robustness under the conditions of non-rigid transformation, positioning error, false alarm and missing detection.

Key words: multi-source data association, ship formation target, neural network, adaptive model, spatial position error

中图分类号:

熊振宇, 崔亚奇, 熊伟, 顾祥岐. 卫星与雷达位置数据自适应关联[J]. 系统工程与电子技术, 2021, 43(1): 91-98.

Zhenyu XIONG, Yaqi CUI, Wei XIONG, Xiangqi GU. Adaptive association for satellite and radar position data[J]. Systems Engineering and Electronics, 2021, 43(1): 91-98.

图/表 11

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网络层	输入尺寸	网络参数	输出尺寸
MLN-1	M×N×2	1×1×16	M×N×16
MLN-2	M×N×16	1×1×64	M×N×64
MLN-3	M×N×64	1×1×128	M×N×128
MLN-4	M×N×128	1×1×256	M×N×256
MLN-5	M×N×256	1×1×512	M×N×512
Maxpool	M×N×512	1×N×1	M×1×512

网络层	输入尺寸	网络参数	输出尺寸
MLN-6	M×N×512	1×1×256	M×N×256
MLN-7	M×N×256	1×1×128	M×N×128
MLN-8	M×N×128	1×1×2	M×N×2

方法	平均关联准确率	关联时间
GMM算法(训练)	0.671 7	约32 h
本文算法(训练)	0.846 6	约6 min
GMM算法(测试)	0.663 2	约32 h
本文算法(测试)	0.841 3	约8 s

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