{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T08:01:04Z","timestamp":1770537664463,"version":"3.49.0"},"reference-count":16,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,3,4]],"date-time":"2016-03-04T00:00:00Z","timestamp":1457049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 (\u03bcs) level, which make the proposed approach usable in practical interference monitoring applications.<\/jats:p>","DOI":"10.3390\/s16030329","type":"journal-article","created":{"date-parts":[[2016,3,4]],"date-time":"2016-03-04T10:53:34Z","timestamp":1457088814000},"page":"329","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4806-0056","authenticated-orcid":false,"given":"Wutao","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Zhigang","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Rongling","family":"Lang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Honglei","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Kai","family":"Zhou","sequence":"additional","affiliation":[{"name":"Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yongbin","family":"Cao","sequence":"additional","affiliation":[{"name":"Realsil Microelectronics Inc., Suzhou 215021, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,4]]},"reference":[{"key":"ref_1","first-page":"7535","article-title":"Vulnerability of the GPS Signal to Jamming","volume":"36","author":"Pinker","year":"1999","journal-title":"GPS Solut."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/TAES.2009.5310313","article-title":"A Statistical Inference Technique for GPS Interference Detection","volume":"45","author":"Balaei","year":"2009","journal-title":"IEEE Trans. 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