{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:56:13Z","timestamp":1763643373407,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["(2018YFB0505200 and 2018YFB0505201"],"award-info":[{"award-number":["(2018YFB0505200 and 2018YFB0505201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aiming at the problem of high-precision positioning of mass-pedestrians with low-cost sensors, a robust single-antenna Global Navigation Satellite System (GNSS)\/Pedestrian Dead Reckoning (PDR) integration scheme is proposed with Gate Recurrent Unit (GRU)-based zero-velocity detector. Based on the foot-mounted pedestrian navigation system, the error state extended Kalman filter (EKF) framework is used to fuse GNSS position, zero-velocity state, barometer elevation, and other information. The main algorithms include improved carrier phase smoothing pseudo-range GNSS single-point positioning, GRU-based zero-velocity detection, and adaptive fusion algorithm of GNSS and PDR. Finally, the scheme was tested. The root mean square error (RMSE) of the horizontal error in the open and complex environments is lower than 1 m and 1.5 m respectively. In the indoor elevation experiment where the elevation difference of upstairs and downstairs exceeds 25 m, the elevation error is lower than 1 m. This result can provide technical reference for the accurate and continuous acquisition of public pedestrian location information.<\/jats:p>","DOI":"10.3390\/rs14020300","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T22:03:13Z","timestamp":1641852193000},"page":"300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Robust GNSS\/PDR Integration Scheme with GRU-Based Zero-Velocity Detection for Mass-Pedestrians"],"prefix":"10.3390","volume":"14","author":[{"given":"Dongpeng","family":"Xie","sequence":"first","affiliation":[{"name":"Electronic Information School, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0122-5514","authenticated-orcid":false,"given":"Jinguang","family":"Jiang","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Jiaji","family":"Wu","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9661-3562","authenticated-orcid":false,"given":"Peihui","family":"Yan","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Yanan","family":"Tang","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Jingnan","family":"Liu","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1007\/s10291-017-0612-y","article-title":"Robust GPS\/BDS\/INS tightly coupled integration with atmospheric constraints for long-range kinematic positioning","volume":"21","author":"Han","year":"2017","journal-title":"GPS Solut."},{"key":"ref_2","unstructured":"Kim, Y., Hwang, Y., Choi, S., and Lee, J. (2013, January 9\u201312). Height estimation scheme of low-cost pedestrian dead-reckoning system using Kalman Filter and walk condition estimation algorithm. Proceedings of the 2013 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics, Wollongong, NSW, Australia."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1109\/JSEN.2019.2954532","article-title":"Assessment for INS\/GNSS\/Odometer\/Barometer Integration in Loosely-Coupled and Tightly-Coupled Scheme in a GNSS-Degraded Environment","volume":"20","author":"Chiang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yan, P.H., Jiang, J.G., Zhang, F.N., Xie, D.P., Wu, J.J., Zhang, C., Tang, Y.A., and Liu, J.N. (2021). An Improved Adaptive Kalman Filter for a Single Frequency GNSS\/MEMS-IMU\/Odometer Integrated Navigation Module. Remote Sens., 13.","DOI":"10.3390\/rs13214317"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yan, P.H., Jiang, J.G., Tang, Y.A., Zhang, F.N., Xie, D.P., Wu, J.J., Liu, J.H., and Liu, J.N. (2021). Dynamic Adaptive Low Power Adjustment Scheme for Single-Frequency GNSS\/MEMS-IMU\/Odometer Integrated Navigation in the Complex Urban Environment. Remote Sens., 13.","DOI":"10.3390\/rs13163236"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.inffus.2012.01.009","article-title":"Improving data fusion in personal positioning systems for outdoor environments","volume":"14","author":"Kaufmann","year":"2013","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.inffus.2010.01.003","article-title":"GPS\/INS integration utilizing dynamic neural networks for vehicular navigation","volume":"12","author":"Noureldin","year":"2011","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1109\/LCOMM.2020.3044714","article-title":"Smartphones: 3D Indoor Localization Using Wi-Fi RTT","volume":"25","author":"Cao","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3733","DOI":"10.1109\/JSEN.2019.2894714","article-title":"A Low-Cost INS and UWB Fusion Pedestrian Tracking System","volume":"19","author":"Tian","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Seco, F., and Jim\u00e9nez, A.R. (2018). Smartphone-based cooperative indoor localization with RFID technology. Sensors, 18.","DOI":"10.3390\/s18010266"},{"key":"ref_11","first-page":"1","article-title":"Attitude Determination of Multirotor Aerial Vehicles Using Camera Vector Measurements","volume":"86","author":"Santos","year":"2016","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1017\/S0373463300012492","article-title":"Development and testing of a radar target enhancer for navigation buoys","volume":"48","author":"Ward","year":"1995","journal-title":"J. Navig."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhu, R.W., Wang, Y., Cao, H., Yu, B., Gan, X., Huang, L., Zhang, H., Li, S., Jia, H., and Chen, J. (2020). RTK\/Pseudolite\/LAHDE\/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments. Sensor, 20.","DOI":"10.3390\/s20061791"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24862","DOI":"10.3390\/s151024862","article-title":"A Bluetooth\/PDR Integration Algorithm for an Indoor Positioning System","volume":"15","author":"Xin","year":"2015","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cavallo, F., Sabatini, A.M., and Genovese, V. (2005, January 2\u20136). A step toward GPS\/INS personal navigation systems: Real-time assessment of gait by foot inertial sensing. Proceedings of the 2005 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada.","DOI":"10.1109\/IROS.2005.1544967"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Polak, L., Rozum, S., Slanina, M., Bravenec, T., Fryza, T., and Pikrakis, A. (2021). Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning. Sensors, 21.","DOI":"10.3390\/s21134605"},{"key":"ref_17","first-page":"43","article-title":"Foot-mounted pedestrian navigation technology based on tightly coupled PDR\/UWB","volume":"36","author":"Sun","year":"2017","journal-title":"Transducer Microsyst. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3234","DOI":"10.1109\/TIE.2011.2148671","article-title":"Towards Miniaturization of a MEMS-Based Wearable Motion Capture System","volume":"58","author":"Brigante","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.inffus.2017.04.006","article-title":"Inertial\/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion","volume":"39","author":"Qiu","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1109\/TIM.2018.2869262","article-title":"A Robust Pedestrian Dead Reckoning System Using Low-Cost Magnetic and Inertial Sensors","volume":"68","author":"Shi","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, Z., Hu, X., Wu, M., and Cao, J. (2013). Constrained total least-squares calibration of three-axis magnetometer for vehicular applications. Meas. Sci. Technol., 24.","DOI":"10.1088\/0957-0233\/24\/9\/095003"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.2514\/1.56726","article-title":"Attitude-Independent Magnetometer Calibration with Time-Varying Bias","volume":"35","author":"Springmann","year":"2011","journal-title":"J. Guid. Control Dyn."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4762","DOI":"10.1109\/TIE.2017.2652342","article-title":"Analysis and Calibration of the Nonorthogonal Angle in Dual-Axis Rotational INS","volume":"64","author":"Deng","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Shkel, A.M. (2019). Adaptive Threshold for Zero-Velocity Detector in ZUPT-Aided Pedestrian Inertial Navigation. IEEE Sens. Lett., 3.","DOI":"10.1109\/LSENS.2019.2946129"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MCG.2005.140","article-title":"Pedestrian Tracking with Shoe-Mounted Inertial Sensors","volume":"25","author":"Foxlin","year":"2005","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Woyano, F., Lee, S., and Park, S. (February, January 31). Evaluation and comparison of performance analysis of indoor inertial navigation system based on foot mounted IMU. Proceedings of the 2016 18th International Conference on Advanced Communication Technology (ICACT), Phoenix Park, PyeongChang, Korea.","DOI":"10.1109\/ICACT.2016.7423561"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1109\/TBME.2010.2060723","article-title":"Zero-Velocity Detection\u2014An Algorithm Evaluation","volume":"57","author":"Skog","year":"2010","journal-title":"IEEE Trans. Bio-Med. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.1109\/TMECH.2015.2430357","article-title":"Stance-Phase Detection for ZUPT-Aided Foot-Mounted Pedestrian Navigation System","volume":"20","author":"Wang","year":"2015","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, H.f., Ren, W., Zhang, T., Gong, J., Liang, J.M., Liu, B., Shi, J.W., and Huang, Z. (2014, January 28\u201330). An adaptive selection algorithm of threshold value in zero velocity updating for personal navigation system. Proceedings of the 33rd Chinese Control Conference, Nanjing, China.","DOI":"10.1109\/ChiCC.2014.6896770"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, Q., Guo, Z., Sun, Z., Cui, X., and Liu, K. (2018). Research on the Forward and Reverse Calculation Based on the Adaptive Zero-Velocity Interval Adjustment for the Foot-Mounted Inertial Pedestrian-Positioning System. Sensors, 18.","DOI":"10.3390\/s18051642"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Callmer, J., T\u00f6rnqvist, D., and Gustafsson, F. (2010, January 26\u201329). Probabilistic stand still detection using foot mounted IMU. Proceedings of the 2010 13th International Conference on Information Fusion, Edinburgh, UK.","DOI":"10.1109\/ICIF.2010.5712024"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wagstaff, B., Peretroukhin, V., and Kelly, J. (2017, January 18\u201321). Improving foot-mounted inertial navigation through real-time motion classification. Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.","DOI":"10.1109\/IPIN.2017.8115947"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wagstaff, B., and Kelly, J. (2018, January 24\u201327). LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation. Proceedings of the 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, France.","DOI":"10.1109\/IPIN.2018.8533770"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"079508","DOI":"10.1360\/SSPMA2015-00144","article-title":"Comparison between CNMC and hatch filter & its precision analysis for BDS precise relative positioning","volume":"45","author":"Guo","year":"2015","journal-title":"Sci. Sin. Phys. Mech. Astron."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"557","DOI":"10.13164\/re.2018.0557","article-title":"GNSS Signals Acquisition and Tracking in Unfavorable Environment","volume":"27","author":"Chebir","year":"2018","journal-title":"Radioengineering"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1017\/S0373463308004694","article-title":"Optimal Hatch Filter with an Adaptive Smoothing Window Width","volume":"61","author":"Park","year":"2008","journal-title":"J. Navig."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.neunet.2018.09.002","article-title":"Soft plus Hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection","volume":"108","author":"Fernando","year":"2018","journal-title":"Neural Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","article-title":"LSTM network: A deep learning approach for short-term traffic forecast","volume":"11","author":"Zheng","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_39","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/300\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:38:30Z","timestamp":1760362710000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,10]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14020300"],"URL":"https:\/\/doi.org\/10.3390\/rs14020300","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,1,10]]}}}