{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T22:11:52Z","timestamp":1777587112702,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,6]],"date-time":"2018-11-06T00:00:00Z","timestamp":1541462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002560","name":"Soonchunhyang University","doi-asserted-by":"publisher","award":["20170812"],"award-info":[{"award-number":["20170812"]}],"id":[{"id":"10.13039\/501100002560","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2018041729"],"award-info":[{"award-number":["2018041729"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The reliability of a navigation system is crucial for navigation purposes, especially in areas where stringent performance is required, such as civil aviation or intelligent transportation systems (ITSs). Therefore, integrity monitoring is an inseparable part of safety-critical navigation applications. The receiver autonomous integrity monitor (RAIM) has been used with the global navigation satellite system (GNSS) to provide integrity monitoring within avionics itself, such as in civil aviation for lateral navigation (LNAV) or the non-precision approach (NPA). However, standard RAIM may not meet the stricter aviation availability and integrity requirements for certain operations, e.g., precision approach flight phases, and also is not sufficient for on-ground vehicle integrity monitoring of several specific ITS applications. One possible way to more clearly distinguish anomalies in observed GNSS signals is to take advantage of time-delayed neural networks (TDNNs) to estimate useful information about the faulty characteristics, rather than simply using RAIM alone. Based on the performance evaluation, it was determined that this method can reliably detect flaws in navigation satellites significantly faster than RAIM alone, and it was confirmed that TDNN-based integrity monitoring using RAIM is an encouraging alternative to improve the integrity assurance level of RAIM in terms of GNSS anomaly detection.<\/jats:p>","DOI":"10.3390\/s18113800","type":"journal-article","created":{"date-parts":[[2018,11,7]],"date-time":"2018-11-07T03:45:22Z","timestamp":1541562322000},"page":"3800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Improvement of Anomalous Behavior Detection of GNSS Signal Based on TDNN for Augmentation Systems"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0816-8137","authenticated-orcid":false,"given":"Daehee","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Internet of Things, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5162-1745","authenticated-orcid":false,"given":"Jeongho","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/j.2161-4296.1988.tb00955.x","article-title":"Autonomous GPS integrity monitoring using the pseudorange residual","volume":"35","author":"Parkinson","year":"1988","journal-title":"Navig. J. Inst. Navig."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1002\/j.2161-4296.1988.tb00975.x","article-title":"Navigation system integrity monitoring using redundant measurements","volume":"35","author":"Sturza","year":"1988","journal-title":"Navig. J. Inst. Navig."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/TITS.2012.2187196","article-title":"Map-aided integrity monitoring of a land vehicle navigation system","volume":"13","author":"Velaga","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","first-page":"1","article-title":"Context-aware GPS integrity monitoring for intelligent transport systems","volume":"3","author":"Binjammaz","year":"2016","journal-title":"J. Traffic Transp. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1002\/j.2161-4296.1986.tb01485.x","article-title":"GPS failure detection by autonomous means within the cockpit","volume":"33","author":"Brown","year":"1986","journal-title":"Navig. J. Inst. Navig."},{"key":"ref_6","unstructured":"Misra, P., and Enge, P. (2006). Global Position System: Signals, Measurements, and Performance, Ganga-Jamuna Press."},{"key":"ref_7","unstructured":"Yang, L., Zhang, Y., and Gao, Y. (2013, January 23\u201325). Enhanced RAIM based on weighted and subset schemes for GNSS receiver. Proceedings of the IEEE\/ION PNT, Honolulu, HI, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.actaastro.2012.03.010","article-title":"The clock-aided RAIM method and its application in improving the positioning precision of GPS receiver","volume":"77","author":"Shi","year":"2012","journal-title":"Acta Astronaut."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1002\/navi.28","article-title":"New advanced RAIM with improved availability for detecting constellation-wide faults, using two independent constellations","volume":"60","author":"Lee","year":"2013","journal-title":"Navig. J. Inst. Navig."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"22854","DOI":"10.3390\/s150922854","article-title":"Vision-aided RAIM: A new method for GPS integrity monitoring in approach and landing phase","volume":"15","author":"Fu","year":"2015","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"25","DOI":"10.4236\/jcc.2015.36004","article-title":"Integrity monitoring in navigation systems: Fault detection and exclusion RAIM algorithm implementation","volume":"3","author":"Rakipi","year":"2015","journal-title":"J. Comput. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Borio, D., and Gioia, C. (2016). Galileo: The added value for integrity in harsh environments. Sensors, 16.","DOI":"10.3390\/s16010111"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1016\/j.asr.2017.12.016","article-title":"Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm","volume":"61","author":"Wang","year":"2018","journal-title":"Adv. Space Res."},{"key":"ref_14","unstructured":"Martini, I., Rippl, M., and Meurer, M. (2013, January 16\u201320). Advanced RAIM Architecture Design and User Algorithm Performance in a Real GPS, GLONASS and Galileo Scenario. Proceedings of the IEEE\/ION ITM, Nashville, TN, USA."},{"key":"ref_15","unstructured":"Belabbas, B., and Gass, F. (2005, January 13\u201316). RAIM Algorithms Analysis for a Combined GPS\/GALILEO Constellation. Proceedings of the IEEE\/ION ITM, Long Beach, CA, USA."},{"key":"ref_16","first-page":"2563202","article-title":"Research on GNSS Receiver Autonomous Integrity Monitoring Method Based on M-Estimation","volume":"2018","author":"Zhang","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MAES.2015.140024","article-title":"GNSS Reliability and Positioning Accuracy Enhancement Based on Fast Satellite Selection Algorithm and RAIM in Multiconstellation","volume":"30","author":"Meng","year":"2015","journal-title":"IEEE A&E Syst. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.actaastro.2016.06.046","article-title":"On the enhanced detectability of GPS anomalous behavior with relative entropy","volume":"127","author":"Cho","year":"2016","journal-title":"Acta Astronaut."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/TNN.2007.899702","article-title":"Identification of nonlinear systems with unknown time delay based on time-delay neural networks","volume":"18","author":"Ren","year":"2007","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1109\/TNN.2009.2024679","article-title":"Processing short-term and long-term information with a combination of polynomial approximation techniques and time-delay neural networks","volume":"20","author":"Fuchs","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1109\/TCYB.2015.2418092","article-title":"Time-delay neural network for continuous emotional dimension prediction from facial expression sequences","volume":"46","author":"Meng","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wohler, C., and Anlauf, J.K. (1999). A time delay neural network algorithm for estimating image-pattern shape and motion. Image Vis. Comput., 281\u2013294.","DOI":"10.1016\/S0262-8856(98)00108-5"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Peddinti, V., Povey, D., and Khudanpur, S. (2015, January 6\u201310). A Time Delay Neural Network Architecture for Efficient Modeling of Long Temporal Contexts. Proceedings of the INTERSPEECH, Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-647"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (arxiv, 2014). Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition, arxiv.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Hinton, G. (2013, January 26\u201331). Speech Recognition with Deep Recurrent Neural Networks. Proceedings of the IEEE Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/29.21701","article-title":"Phoneme recognition using time-delay neural networks","volume":"37","author":"Waibel","year":"1989","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_28","first-page":"301","article-title":"Conjugate gradient algorithm for efficient training of artificial neural networks","volume":"139","author":"Charalambous","year":"1992","journal-title":"IEE Proc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1162\/neco.1992.4.2.141","article-title":"First- and Second-Order Methods for Learning: Between Steepest Descent and Newton\u2019s Method","volume":"4","author":"Battiti","year":"1992","journal-title":"Neural Comput."},{"key":"ref_30","unstructured":"Wilamowski, B.M., and Chen, Y. (1999, January 10\u201316). Efficient Algorithm for Training Neural Networks with one Hidden Layer. Proceedings of the IJCNN, Washington, DC, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Training feed forward network with the Marquardt algorithm","volume":"5","author":"Hagan","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_32","first-page":"1873","article-title":"Acceleration of Levenberg-Marquadt training of neural networks with variable decay rate","volume":"3","author":"Chen","year":"2003","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/TAES.2014.130498","article-title":"GBAS ionospheric anomaly monitoring strategy using Kullback-Leibler divergence metric","volume":"51","author":"Cho","year":"2015","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yun, Y., Cho, J., and Heo, M. (2012, January 23\u201326). Automated determination of fault detection thresholds for integrity monitoring algorithms of GNSS augmentation systems. Proceedings of the IEEE\/ION PLANS, Myrtle Beach, SC, USA.","DOI":"10.1109\/PLANS.2012.6236969"},{"key":"ref_35","unstructured":"Radio Technical Commission for Aeronautics (RTCA) (2006). Minimum Operational Performance Standards for Global Positioning System\/Wide Area Augmentation System Airborne Equipment, RTCA. RTCA Document No. RTCA\/DO-229D."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3800\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:28:18Z","timestamp":1760196498000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,6]]},"references-count":35,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113800"],"URL":"https:\/\/doi.org\/10.3390\/s18113800","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,6]]}}}