基于多源卫星遥感数据的舰船航迹关联与融合

doi:10.12305/j.issn.1001-506X.2026.04.06

Abstract

Abstract:

To address the problem of ship tracks association and fusion for multi-sensor data, integrating electronic reconnaissance, radar, and synthetic aperture radar data, a solution combining graph matching network （GMN） and Transformer architecture is proposed. Utilize a dual-graph modeling framework and cross-graph convolutional network to capture complex relationships between sensor data and transform the track association problem into a quadratic programming task, a weighted binary cross-entropy loss function is introduced to reduce mismatches effectively. In the fusion stage, learnable positional encoding and a sliding window mechanism are applied to reduce computational complexity, while the decoder leverages multi-head attention mechanisms to enhance temporal dependency modeling. Experimental results show that the proposed method significantly outperforms traditional approaches in data association accuracy and fused track prediction precision, offering innovative technical support for multi-sensor data association and fusion.

Key words: multi-source satellite, track association, track fusion, graph matching network, Transformer

CLC Number:

V 24

Xinsheng LI, Shuyi FENG, Yuzhe HAO, Xi YE, Haichao ZHANG, Yuanxiang LI. Association and fusion of ship tracks based on multi-source satellite remote sensing data[J]. Systems Engineering and Electronics, 2026, 48(4): 1154-1164.

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References 31

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算法	σ
算法	0.5	0.6	0.7	0.8	0.9
模糊理论算法	13.3	14.8	22.5	11.1	12.4
混合序列相似度算法	11.8	10.3	9.7	8.5	8.7
文献[9]	11.2	8.62	7.54	7.27	7.66
GMN算法	9.6	8.4	7.9	6.3	6.54

算法	σ
算法	0.5	0.6	0.7	0.8	0.9
模糊理论算法	64.5	67.1	69.8	71.2	72.5
混合序列相似度算法	71.6	74.4	79.4	76.1	78.2
文献[9]	73.5	76.8	88.6	89.7	87.1
GMN算法	76.8	82.3	87.7	90.2	88.6

算法	场景
算法	5 km×5 km	10 km×10 km	20 km×20 km
模糊理论算法	12.5	6.8	5.4
混合序列相似度算法	9.7	6.3	4.6
文献[9]	8.23	5.78	4.01
GMN算法	7.4	5.1	3.2

场景	凸组合融合算法	CI 融合算法	LSTM+Attention	本文方法
5×5	9.46	12.66	7.53	5.36
10×10	7.21	8.53	6.11	3.32
20×20	4.13	6.42	3.54	1.81

场景	凸组合融合算法	CI 融合算法	LSTM+Attention	本文方法
5×5	2.53	2.4	2.03	1.86
10×10	2.46	2.25	1.97	1.82
20×20	2.27	2.02	1.94	1.79

Association and fusion of ship tracks based on multi-source satellite remote sensing data

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