{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T08:08:13Z","timestamp":1781338093418,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,9]],"date-time":"2018-11-09T00:00:00Z","timestamp":1541721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1201500"],"award-info":[{"award-number":["2018YFB1201500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["2017YJS030"],"award-info":[{"award-number":["2017YJS030"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the China Scholarship Council","award":["Grant No. CSC 201507090010"],"award-info":[{"award-number":["Grant No. CSC 201507090010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The diverse operating environments change GNSS measurement noise covariance in real time, and different GNSS techniques hold different measurement noise covariance as well. Mismodelling the covariance causes undependable filtering results and even degenerates the GNSS\/INS Particle Filter (PF) process, due to the fact that INS error-state noise covariance is much smaller than that of GNSS measurement noise. It also makes the majority of existing methods for adaptively adjusting filter parameters incapable of performing well. In this paper, a feasible Digital Track Map-aided (DTM-aided) adaptive extended Kalman particle filter method is introduced in GNSS\/INS integration in order to adjust GNSS measurement noise covariance in real time, and the GNSS down-direction offset is also estimated along with every sampling period through making full use of DTM information. The proposed approach is successfully examined in a railway environment, and the on-site experimental results reveal that the adaptive approach holds better positioning performance in comparison to the methods without adaptive adjustment. Improvements of 62.4% and 14.9% in positioning accuracy are obtained in contrast to Standard Point Positioning (SPP) and Precise Point Positioning (PPP), respectively. The proposed adaptive method takes advantage of DTM information and is able to automatically adapt to complex railway environments and different GNSS techniques.<\/jats:p>","DOI":"10.3390\/s18113860","type":"journal-article","created":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T03:27:31Z","timestamp":1542079651000},"page":"3860","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DTM-Aided Adaptive EPF Navigation Application in Railways"],"prefix":"10.3390","volume":"18","author":[{"given":"Chengming","family":"Jin","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baigen","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Computing, Beijing Jiaotong University, Beijing 100044, China"},{"name":"State Key Laboratory of Railway Traffic Control &amp; Safety, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allison","family":"Kealy","sequence":"additional","affiliation":[{"name":"School of Geospatial Science, Royal Melbourne Institute of Technology, Melbourne VIC 3001, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.trc.2011.12.002","article-title":"Simulation-based evaluation of dependability and safety properties of satellite technologies for railway localization","volume":"22","author":"Beugin","year":"2012","journal-title":"Transp. Res. Part C"},{"key":"ref_2","unstructured":"Neri, A., Sabina, S., Rispoli, F., and Mascia, U. (2015, January 14\u201318). GNSS and Odometry Fusion for High Integrity and High Availability Train Control Systems. Proceedings of the ION GNSS+ 2015, Tampa, FL, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Stallo, C., Neri, A., Salvatori, P., Coluccia, A., Capua, R., Olivieri, G., Gattuso, L., Bonenberg, L., Moore, T., and Rispoli, F. (2018, January 23\u201326). GNSS-based Location Determination System Architecture for Railway Performance Assessment in Presence of Local Effects. Proceedings of the IEEE\/ION Plans 2018, Monterey, CA, USA.","DOI":"10.1109\/PLANS.2018.8373403"},{"key":"ref_4","first-page":"1","article-title":"A Survey of GNSS-Based Research and Developments for the European Railway Signaling","volume":"99","author":"Marais","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1017\/S0373463312000082","article-title":"Multi-Constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models","volume":"65","author":"Wang","year":"2012","journal-title":"J. Navig."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Geng, T., Su, X., Fang, R., Xie, X., Zhao, Q., and Liu, J. (2016). BDS Precise Point Positioning for Seismic Displacements Monitoring: Benefit from the High-Rate Satellite Clock Corrections. Sensors, 16.","DOI":"10.3390\/s16122192"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, Z., Zhao, J., Zhou, K., Wang, Z., and Yuan, H. (2016). Smart Device-Supported BDS\/GNSS Real-Time Kinematic Positioning for Sub-Meter-Level Accuracy in Urban Location-Based Services. Sensors, 16.","DOI":"10.3390\/s16122201"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1002\/j.2161-4296.2010.tb01777.x","article-title":"Performance Comparison of Different Forms of Kalman Filter Approaches for a Vector-Based GNSS Signal Tracking Loop","volume":"57","author":"Won","year":"2014","journal-title":"J. Inst. Navig."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10291-016-0565-6","article-title":"Performance analysis and design of the optimal frequency-assisted phase tracking loop","volume":"21","author":"Jiang","year":"2017","journal-title":"GPS Solut."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Falco, G., Pini, M., and Marucco, G. (2017). Loose and Tight GNSS\/INS Integrations: Comparison of Performance Assessed in Real Urban Scenarios. Sensors, 17.","DOI":"10.3390\/s17020255"},{"key":"ref_11","unstructured":"Ruotsalainen, L., Kirkko-Jaakkola, M., Bhuiyan, Z., S\u00f6derholm, S., Thombre, S., and Kuusniemi, H. (2014, January 8\u201312). Deeply Coupled GNSS, INS and Visual Sensor Integration for Interference Mitigation. Proceedings of the ION GNSS+ 2014, Tampa, FL, USA."},{"key":"ref_12","unstructured":"Groves, P.D. (2013). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Artech House."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3768","DOI":"10.3390\/s140203768","article-title":"Implementation and Performance of a GPS\/INS Tightly Coupled Assisted PLL Architecture Using MEMS Inertial Sensors","volume":"14","author":"Tawk","year":"2014","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isatra.2018.02.005","article-title":"Nonlinear system identification based on Takagi-Sugeno fuzzy modelling and unscented Kalman filter","volume":"74","author":"Vafamand","year":"2018","journal-title":"ISA Trans."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1109\/TLA.2018.8327386","article-title":"Estimation of complex systems with parametric uncertainties using a JSSF heuristically adjusted","volume":"16","year":"2018","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A new approach to linear filtering and prediction problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7444","DOI":"10.1016\/j.jfranklin.2017.08.038","article-title":"Stable Kalman filter and neural network for the chaotic systems identification","volume":"354","year":"2017","journal-title":"J. Frankl. Inst."},{"key":"ref_18","first-page":"79","article-title":"A novel algorithm for the modelling of complex processes","volume":"54","author":"Rubio","year":"2018","journal-title":"Kybernetika"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., and Nordlund, P.J. (2001). Particle Filters for Positioning, Navigation and Tracking, Link\u00f6ping University Electronic Press.","DOI":"10.1109\/78.978396"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ghirmai, T. (2016). Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications. Sensors, 16.","DOI":"10.3390\/s16091454"},{"key":"ref_21","unstructured":"Giremus, A., and Tourneret, J.-Y. (2006, January 4\u20138). Controlling particle filter regularization for GPS\/INS hybridization. Proceedings of the 2006 14th European Signal Processing Conference, Florence, Italy."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7228","DOI":"10.3390\/s150407228","article-title":"Integration of GPS precise point positioning and MEMS-based INS using unscented particle filter","volume":"15","author":"Rabbou","year":"2015","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1017\/S0373463310000068","article-title":"INS\/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter","volume":"63","author":"Zhou","year":"2010","journal-title":"J. Navig."},{"key":"ref_24","unstructured":"Gebre-Egziabher, D., and Gleason, S. (2009). GNSS Applications and Methods, Artech House."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/78.978383","article-title":"Particle filters for state-space models with the presence of unknown static parameters","volume":"50","author":"Storvik","year":"2002","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fox, D. (2002, January 9\u201314). KLD-sampling: Adaptive particle filters. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, UK.","DOI":"10.7551\/mitpress\/1120.003.0096"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1016\/j.automatica.2013.02.046","article-title":"Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters","volume":"49","author":"Saha","year":"2013","journal-title":"Automatica"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rabiei, E., Droguett, E.L., and Modarres, M. (2018). Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis. Entropy, 20.","DOI":"10.3390\/e20020100"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1177\/0278364903022012001","article-title":"Adapting the sample size in particle filters through KLD-sampling","volume":"22","author":"Fox","year":"2003","journal-title":"Int. J. Robot. Res."},{"key":"ref_30","unstructured":"Liu, Z., Shi, Z., Zhao, M., and Xu, W. (November, January 29). Mobile robots global localization using adaptive dynamic clustered particle filters. Proceedings of the 2007 IEEE\/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1163\/016918609X12496340121133","article-title":"New Entropy-Based Adaptive Particle Filter for Mobile Robot Localization","volume":"23","author":"Cen","year":"2009","journal-title":"Adv. Robot."},{"key":"ref_32","unstructured":"Evennou, F., Marx, F., and Novakov, E. (2005, January 13\u201317). Map-aided indoor mobile positioning system using particle filter. Proceedings of the 2005 IEEE Wireless Communications and Networking Conference, New Orleans, LA, USA."},{"key":"ref_33","unstructured":"Heirich, O., Robertson, P., Garcia, A.C., and Strang, T. (2012, January 9\u201312). Bayesian train localization method extended by 3D geometric railway track observations from inertial sensors. Proceedings of the 2012 15th International Conference on Information Fusion, Singapore."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, J., Cai, B.-G., and Wang, J. (2016, January 19\u201322). Track-constrained GNSS\/odometer-based train localization using a particle filter. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535491"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gordon, N.J., Salmond, D.J., and Smith, A.F.M. (1993). Novel approach to nonlinear\/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), IET.","DOI":"10.1049\/ip-f-2.1993.0015"},{"key":"ref_36","unstructured":"Ristic, B., Arulampalam, S., and Gordon, N. (2003). Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1023\/A:1008935410038","article-title":"On sequential Monte Carlo sampling methods for Bayesian filtering","volume":"10","author":"Doucet","year":"2000","journal-title":"Stat. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1080\/01621459.1998.10473765","article-title":"Sequential Monte Carlo Methods for Dynamic Systems","volume":"93","author":"Liu","year":"1998","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3860\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:28:58Z","timestamp":1760196538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3860"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,9]]},"references-count":38,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113860"],"URL":"https:\/\/doi.org\/10.3390\/s18113860","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,9]]}}}