基于Transformer多参数加权融合的GNSS欺骗干扰检测

doi:10.12305/j.issn.1001-506X.2026.07.27

Abstract

Abstract:

To address the insufficient scenario adaptability of conventional global navigation satellite system （GNSS） spoofing detection methods and the challenges of high false alarm rates and poor generalization in existing machine learning models, a GNSS spoofing interference detection method based on Transformer multi-parameter weighted fusion is proposed. The method considers abnormal parameter variations induced by spoofing interference during the tracking phase, and employs multi-parameter weighted fusion of correlator output metrics, carrier-to-noise ratio and other multidimensional features to enhance the model’s sensitivity to characteristic perturbations. Furthermore, feature dimension compression effectively improves model training efficiency. Experimental results demonstrate that the proposed method achieves 100% detection accuracy and F1-Score across three distinct datasets: TEXBAT, OAKBAT, and FGI-SpoofRepo. Compared with conventional hypothesis testing methods and support vector machine models, the proposed method significantly enhances both detection accuracy and robustness against spoofing interference. The developed multi-parameter weighted fusion mechanism establishes novel theoretical foundations for subsequent anti-jamming receiver design.

Key words: satellite navigation, spoofing detection, multi-parameter weighted fusion, Transformer

CLC Number:

TN 97

Zhiyu HAO, Xiaohui LIU, Xiaoyu ZHAO, Zongnan LI, Zukun LU. GNSS spoofing interference detection based on Transformer multi-parameter weighted fusion[J]. Systems Engineering and Electronics, 2026, 48(7): 2434-2447.

Figures/Tables 19

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References 31

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参数名称	参数值
编码器层数	3
多头注意力机制的头数	4
模型维度（嵌入维度）	128
前馈神经网络层的隐藏维度	128
批量大小	64
全连接层前的全局Dropout率	0.3

数据集名称	${w_{11}}$	${w_{12}}$	${w_{13}}$	${w_{14}}$
Texbat-ds2	0.1095	0.5349	0.2381	0.1175
Texbat-ds5	0.1797	0.2567	0.3843	0.1793
Oakbat-os3	0.2470	0.2553	0.2507	0.2470
FGI_TGD	0.2450	0.2441	0.2600	0.2509

数据集名称	${w_{21}}$	${w_{22}}$	${w_{23}}$	${w_{24}}$
Texbat-ds2	0.3707	0.3789	0.1661	0.0844
Texbat-ds5	0.8152	0.0474	0.0869	0.0505
Oakbat-os3	0.0254	0.0798	0.7911	0.7911
FGI_TGD	0.1913	0.1649	0.3278	0.3161

参数组合	参数选择
Case 1	${h_9}(n)$
Case 2	${h_{10}}(n)$
Case 3	$ {h_1}(n) + {h_2}(n) + {h_3}(n) + {h_4}(n) $
Case 4	${h_9}(n) + {h_{10}}(n)$
Case 5	$ {h_5}(n) + {h_6}(n) + {h_7}(n) + {h_8}(n) $
Case 6	${h_{10}}(n) + {\mathrm{Doppler}} + {\mathrm{carrPhase}} $
Case 7	${h_9}(n) + {h_{10}}(n) + {\mathrm{Doppler}} $
Case 8	$ h_1(n) \sim h_{10}(n)+ { {\mathrm{Doppler}} }+C / N_0+ { {\mathrm{carrPhase}} }$

卫星PRN	假设检验	Transformer	卫星PRN	假设检验	Transformer
GPS-03	0.92	0.97	GPS-16	0.81	0.97
GPS-06	0.76	0.97	GPS-19	0.72	0.93
GPS-13	0.93	0.98	GPS-23	0.96	0.99

真实标签	预测标签
真实标签	欺骗信号	真实信号
欺骗信号	TP	FN
真实信号	FP	TN

GNSS spoofing interference detection based on Transformer multi-parameter weighted fusion

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References 31

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参数组合	运行时间/s	准确率/%	精确率/%	召回率/%	F1-Score/%	虚警率/%	Kappa系数
SVM-Case 1	328.75	91	89	95	91	11	0.84
SVM-Case 2	326.49	79	92	62	74	5	0.58
SVM-Case 3	395.25	92	90	94	92	10	0.84
SVM-Case 4	442.54	94	93	95	94	7	0.88
SVM-Case 5	388.59	100	100	100	100	0	1.00
SVM-Case 6	435.26	99.48	99.35	99.60	99.47	0.63	0.99
SVM-Case 7	478.85	99.99	99.98	100	99.99	0.02	0.9998
SVM-Case 8	519.81	99.80	99.60	100	99.80	0.39	0.9961
Transformer-Case 1	326.35	92	90	94	92	10	0.85
Transformer-Case 2	350.12	81	96	63	76	3	0.61
Transformer-Case 3	367.19	93	91	95	93	9	0.85
Transformer-Case 4	398.06	95	93	96	95	7	0.89
Transformer-Case 5	336.79	100	100	100	100	0	1.00
Transformer-Case 6	400.99	100	100	100	100	0	1.00
Transformer-Case 7	396.82	100	100	100	100	0	1.00
Transformer-Case 8	404.43	100	100	100	100	0	1.00

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