{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:28:56Z","timestamp":1780511336102,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T00:00:00Z","timestamp":1547683200000},"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>The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU\/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.<\/jats:p>","DOI":"10.3390\/s19020364","type":"journal-article","created":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T11:30:27Z","timestamp":1547724627000},"page":"364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU\/Ultrasonic Sensor"],"prefix":"10.3390","volume":"19","author":[{"given":"Ming","family":"Xia","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chundi","family":"Xiu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongkai","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"Earth Observation System and Data Center, China National Space Administration, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/SURV.2012.121912.00075","article-title":"A survey of indoor inertial positioning systems for pedestrians","volume":"15","author":"Harle","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1109\/TITS.2016.2516822","article-title":"Positioning techniques in indoor environments based on stochastic modeling of UWB round-trip-time measurements","volume":"17","author":"Angelis","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/JPROC.2008.2008846","article-title":"Ranging with ultrawide bandwidth signals in multipath environments","volume":"97","author":"Dardari","year":"2009","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8358","DOI":"10.3390\/s150408358","article-title":"A novel method for constructing a WIFI positioning system with efficient manpower","volume":"15","author":"Du","year":"2015","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/TMC.2011.243","article-title":"SSD: A robust RF location fingerprint addressing mobile devices\u2019 heterogeneity","volume":"12","author":"Hossain","year":"2013","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1109\/TITB.2012.2204896","article-title":"Equipment location in hospitals using RFID-based positioning system","volume":"16","author":"Shirehjini","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6509","DOI":"10.1007\/s11277-017-4852-5","article-title":"Advanced indoor positioning using Zigbee wireless technology","volume":"97","author":"Uradzinski","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ilyas, M., Cho, K., Baeg, S.H., and Park, S. (2016). Drift reduction in pedestrian navigation system by exploiting motion constraints and magnetic field. Sensors, 16.","DOI":"10.3390\/s16091455"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MWC.2011.5751293","article-title":"Indoor navigation with foot-mounted strapdown inertial navigation and magnetic sensors","volume":"18","author":"Bird","year":"2011","journal-title":"Wirel. Commun. IEEE."},{"key":"ref_10","unstructured":"Rahim, K.A. (2012). Heading Drift Mitigation for Low-Cost Inertial Pedestrian Navigation. [Ph.D. Thesis, University of Nottingham]."},{"key":"ref_11","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. Gr. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez, A.R., Seco, F., Prieto, J.C., and Guevara, J. (2010, January 11\u201312). Indoor pedestrian navigation using an INS\/EKF framework for yaw drift reduction and a foot-mounted IMU. Proceedings of the Workshop on Positioning Navigation and Communication, Dresden, Germany.","DOI":"10.1109\/WPNC.2010.5649300"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1109\/JSEN.2017.2665678","article-title":"Adaptive zero velocity update based on velocity classification for pedestrian tracking","volume":"17","author":"Zhang","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1109\/TIM.2011.2179830","article-title":"Estimation of human foot motion during normal walking using inertial and magnetic sensor measurements","volume":"61","author":"Yun","year":"2012","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, S.Z., Yu, S., Liu, C.J., Yuan, X., and Liu, S. (2016). A dual-linear Kalman filter for real-time orientation determination system using low-cost MEMS sensors. Sensors, 16.","DOI":"10.3390\/s16020264"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, W., Xiu, C.D., Zhang, J.M., and Yang, D. (2017). A novel 3D pedestrian navigation method for a multiple Sensors-Based Foot-Mounted inertial system. Sensors, 17.","DOI":"10.3390\/s17112695"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qian, X.M., and Cui, B.T. (2017). H\u221e Filtering in mobile sensor networks with missing measurements and quantization effects. International Symposium on Parallel Architecture, Algorithm and Programming, Springer.","DOI":"10.1007\/978-981-10-6442-5_26"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.1002\/rnc.3201","article-title":"H\u221e state estimation with fading measurements, randomly varying nonlinearities and probabilistic distributed delays","volume":"25","author":"Ding","year":"2015","journal-title":"Int. J. Robust Nonlinear Control."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"42","DOI":"10.5081\/jgps.2.1.42","article-title":"Adaptive Kalman filtering for vehicle navigation","volume":"2","author":"Hu","year":"2003","journal-title":"J. Glob. Position. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, H., and Wu, W. (2017). Strong tracking spherical simplex-radial cubature Kalman filter for maneuvering target tracking. Sensors, 17.","DOI":"10.20944\/preprints201704.0106.v1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TAES.2010.5545180","article-title":"MMSE-based filtering in presence of non-gaussian system and measurement noise","volume":"46","author":"Bilik","year":"2010","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1109\/TIM.2010.2084710","article-title":"Study on innovation adaptive EKF for in-flight alignment of airborne POS","volume":"60","author":"Fang","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","first-page":"2970","article-title":"Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle","volume":"4","author":"Sasiadek","year":"1999","journal-title":"IEEE Int. Conf. Robot. Autom."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10291-005-0146-6","article-title":"Improvement of MEMS-IMU\/GPS performance using fuzzy modeling","volume":"10","author":"Abdelazim","year":"2006","journal-title":"GPS Solut."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.ymssp.2017.06.030","article-title":"Fuzzy adaptive integration scheme for low-cost SINS\/GPS navigation system","volume":"99","author":"Nourmohammadi","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1049\/iet-smt.2009.0065","article-title":"Strapdown inertial navigation system\/astronavigation system data synthesis using innovation-based fuzzy adaptive Kalman filtering","volume":"4","author":"Ali","year":"2010","journal-title":"Iet Sci. Meas. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5365983","DOI":"10.1155\/2016\/5365983","article-title":"A new technique for integrating MEMS-based low-cost IMU and GPS in vehicular navigation","volume":"2016","author":"Navidi","year":"2016","journal-title":"J. Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1017\/S0373463310000184","article-title":"Heuristic drift elimination for personnel tracking systems","volume":"63","author":"Johann","year":"2010","journal-title":"J. Navig."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zizzo, G., and Ren, L. (2017). Position tracking during human walking using an integrated wearable sensing system. Sensors, 17.","DOI":"10.3390\/s17122866"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3296","DOI":"10.1109\/TIM.2010.2047157","article-title":"Orientation estimation using a quaternion-based indirect Kalman filter with adaptive estimation of external acceleration","volume":"59","author":"Suh","year":"2010","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pham, D.D., and Suh, Y.S. (2016). Pedestrian navigation using foot-mounted inertial sensor and LIDAR. Sensors, 16.","DOI":"10.3390\/s16010120"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Muthukrishnan, K., Dulman, S., and Langendoen, K. (2010, January 14\u201315). Towards a rapidly deployable positioning system for emergency responders. Proceedings of the IEEE Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), Kirkkonummi, Finland.","DOI":"10.1109\/UPINLBS.2010.5654049"},{"key":"ref_33","unstructured":"Nilsson, J.O., Skog, I., and Handel, P. (2012, January 13\u201315). A note on the limitations of ZUPTs and the implications on sensor error modeling. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sydney, Australia."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/364\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:26:50Z","timestamp":1760185610000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/364"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,17]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["s19020364"],"URL":"https:\/\/doi.org\/10.3390\/s19020364","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,17]]}}}