Li Wutao, Huang Zhigang, Lang Rongling, Qin Honglei, Zhou Kai, Cao Yongbin
School of Electronics and Information Engineering, Beihang University, Beijing 100191, China.
Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel). 2016 Mar 4;16(3):329. doi: 10.3390/s16030329.
Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications.
干扰会严重降低全球导航卫星系统（GNSS）接收机的性能。作为GNSS任何抗干扰措施的第一步，对GNSS进行干扰监测极其重要且必要。由于干扰监测可被视为一个分类问题，本文提出了一种基于孪生支持向量机（TWSVM）的实时干扰监测技术。建立了TWSVM模型，并通过最小二乘孪生支持向量机（LSTWSVM）算法求解TWSVM。分析干扰监测指标以从受干扰的GNSS信号中提取特征。实验结果表明，所选观测值可作为干扰监测指标。通过使用GPS L1 C/A码信号并与标准支持向量机（SVM）的性能进行比较，验证了所提方法的干扰监测性能。实验结果表明，基于TWSVM的干扰监测比传统SVM快得多。此外，TWSVM的训练时间处于毫秒（ms）级别，监测时间处于微秒（μs）级别，这使得所提方法可用于实际的干扰监测应用。
