基于多特征正交约束的无人机跨视角地理定位方法

doi:10.12305/j.issn.1001-506X.2026.06.27

Abstract

Abstract:

Under limited training data, learning more discriminative features through deep learning remains a challenging task. In this paper, we propose an adaptive orthogonal constraint neural network based on Transformer. This approach aims to enhance the density of the embedding space by dividing the original space into multiple subspaces, thereby facilitating the learning of deeper features. Based on the concept of orthogonal constraints, a method for overlapping subspace separation is proposed, thereby reducing homogeneous information. Furthermore, the weights between subspaces are dynamically adjusted to optimize the samples that are difficult to train due to similarity. Experiments are conducted on the University-1652 dataset, real word measured data and simulated data under different weather conditions, and the results show that the algorithm proposed in this paper improves the performance in localization and navigation tasks by 29.58%/26.52% （R@1/AP） and 21.51%/27.98% （R@1/AP）, respectively, compared with the baseline algorithm. Notwithstanding the constrained training data, the model proposed in this paper learns more robust and discriminative feature representations, facilitating the identification of similar classes and aiding unmanned aerial vehicle in achieving precise localization and navigation.

Key words: unmanned aerial vehicles （UAVs）, cross-view geo-localization, visual Transformer, orthogonal constraints, image retrieval

CLC Number:

TP 391.41

Ruihang LIU, Haiying LIU, Yuchen LIU, Chen CHEN, Tiexiang LI. Diverse features orthogonal constraint-based cross-view geo-localization method for UAVs[J]. Systems Engineering and Electronics, 2026, 48(6): 2072-2080.

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References 30

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方法	无人机→卫星		卫星→无人机
方法	R@1	AP	R@1	AP
University-1652^[24]	58.49	63.13	71.18	58.74
Instance Loss^[25]	58.23	62.91	74.47	59.45
Instance + GeM Pooling^[26]	65.32	69.61	79.03	65.35
LCM^[27]	66.65	70.82	79.89	65.38
LPN^[28]	75.93	79.14	86.45	74.79
SGM^[29]	82.14	84.72	88.16	81.81
FSRA^[1]	84.51	86.71	88.45	83.47
PAAN^[30]	84.51	86.78	91.01	82.28
MIFT^[19]	87.84	89.62	92.30	87.66
本文方法	88.07	89.65	92.69	86.72

算法	无人机→卫星		卫星→无人机
算法	R@1	AP	R@1	AP
无DFOC	90.27	85.15	85.36	87.25
DFOC	92.69	86.72	88.07	89.65

算法	无人机→卫星		卫星→无人机
算法	R@1	AP	R@1	AP
无DW	91.73	84.99	86.27	88.20
DW	92.69	86.72	88.07	89.65

权重	卫星→无人机		无人机→卫星
权重	R@1	AP	R@1	AP
0.1	91.04	86.14	85.91	87.73
0.2	91.61	85.88	87.71	89.36
0.3	92.69	86.72	88.07	89.65
0.4	91.45	85.22	86.70	88.58

任务	R@1	AP
模拟数据→卫星影像	84.62	87.14
模拟数据→无人机影像	91.67	89.49

Diverse features orthogonal constraint-based cross-view geo-localization method for UAVs

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