{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:33:43Z","timestamp":1783438423566,"version":"3.54.6"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jingjing Yan","award":["61701167"],"award-info":[{"award-number":["61701167"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In a complex indoor environment, wireless signals are affected by multiple factors such as reflection, scattering or diffuse reflection of electromagnetic waves from indoor walls and other objects, and the signal strength will fluctuate significantly. For the signal strength and the distance between the unknown nodes and the known nodes are a typical nonlinear estimation problem, and the unknown nodes cannot receive all Access Points (APs) signal strength data, this paper proposes a Particle Filter (PF) indoor position algorithm based on the Kernel Extreme Learning Machine (KELM) reconstruction observation model. Firstly, on the basis of establishing a fingerprint database of wireless signal strength and unknown node position, we use KELM to convert the fingerprint location problem into a machine learning problem and establish the mapping relationship between the location of the unknown node and the wireless signal strength, thereby refocusing construct an observation model of the indoor positioning system. Secondly, according to the measured values obtained by KELM, PF algorithm is adopted to obtain the predicted value of the unknown nodes. Thirdly, the predicted value is fused with the measured value obtained by KELM to locate the position of the unknown nodes. Moreover, a novel control strategy is proposed by introducing a reception factor to deal with the situation that unknown nodes in the system cannot receive all of the AP data, i.e., data loss occurs. This indoor positioning experimental results show that the accuracy of the method is significantly improved contrasted with commonly used PF, GP-PF and other positioning algorithms.<\/jats:p>","DOI":"10.3390\/ijgi11010071","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T08:20:42Z","timestamp":1642407642000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Indoor Positioning Algorithm Based on Reconstructed Observation Model and Particle Filter"],"prefix":"10.3390","volume":"11","author":[{"given":"Li","family":"Ma","sequence":"first","affiliation":[{"name":"School of Computer and Information, Hohai University, Nanjing 211106, China"},{"name":"College of Electrical Engineering, Henan University of Technology, Zhengzhou 450052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hohai University, Nanjing 211106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoliang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhengzhou Institute of Technology, Zhengzhou 450044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Henan University of Technology, Zhengzhou 450052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7896","DOI":"10.3390\/en14237896","article-title":"Testing of Software for the Planning of a Linear Object GNSS Measurement Campaign under Simulated Conditions","volume":"14","author":"Figureiel","year":"2021","journal-title":"Energies"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s10291-018-0702-5","article-title":"Railway Irregularity Measuring using Rauch\u2013Tung\u2013Striebel Smoothed Multi-sensors Fusion System: Quad-GNSS PPP, IMU, odometer, and track gauge","volume":"22","author":"Gao","year":"2018","journal-title":"GPS Solut."},{"key":"ref_3","first-page":"11","article-title":"Determination of the Precise Coordinates of the GPS Reference Station in of a GBAS System in the Air Transport","volume":"22","author":"Krasuski","year":"2020","journal-title":"Commun.-Sci. Lett. Univ. Zilina"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.1109\/LCOMM.2020.3016710","article-title":"Fast Localization with Unknown Transmit Power and Path-Loss Exponent in WSNs Based on RSS Measurements","volume":"24","author":"Song","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Berkvens, R., Peremans, H., and Weyn, M. (2016). Conditional Entropy and Location Error in Indoor Localization Using Probabilistic Wi-Fi Fingerprinting. Sensors, 16.","DOI":"10.3390\/s16101636"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, W., Cheng, Q., Deng, Z., and Jia, M. (2021). C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning. Entropy, 23.","DOI":"10.3390\/e23081004"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1109\/LSP.2021.3082035","article-title":"Robust TDOA Source Localization Based on Lagrange Programming Neural Network","volume":"28","author":"Xiong","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hsu, H.H., Peng, W.J., Shih, T.K., Pai, T.W., and Man, K.L. (2015, January 10\u201312). Smartphone Indoor Localization with Accelerometer and Gyroscope. Proceedings of the International Conference on Network Based Information Systems Nbis, Salerno, Italy.","DOI":"10.1109\/NBiS.2014.72"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zaib, S., Khusro, S., Ali, S., and Alam, F. (2019, January 24\u201325). Smartphone Based Indoor Navigation for Blind Persons using User Profile and Simplified Building Information Model. Proceedings of the 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Swat, Pakistan.","DOI":"10.1109\/ICECCE47252.2019.8940799"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"57036","DOI":"10.1109\/ACCESS.2020.2982283","article-title":"The Technology of Crowd-sourcing Landmarks-assisted Smartphone in Indoor Localization","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s13638-019-1462-9","article-title":"Robot Indoor Location Modeling and Simulation based on Kalman Filtering","volume":"2019","author":"Lu","year":"2019","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, J., Ou, G., Peng, A., Zheng, L., and Shi, J. (2018). An INS\/WiFi Indoor Localization System Based on the Weighted Least Squares. Sensors, 18.","DOI":"10.3390\/s18051458"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"837070","DOI":"10.1155\/2015\/837070","article-title":"Multitarget Tracking by Particle Filtering Based on RSS Measurement in Wireless Sensor Networks","volume":"11","author":"Lim","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_14","unstructured":"Hong, F., Zhang, Y., Zhang, Z., Wei, M., Feng, Y., and Guo, Z. (2014, January 8\u201311). WaP: Indoor Localization and Tracking using WiFi-Assisted Particle Filter. Proceedings of the IEEE Conference on Local Computer Networks, Edmonton, AB, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1109\/JIOT.2015.2495229","article-title":"A Feature Scaling based k-Nearest Neighbor Algorithm for Indoor Positioning Systems","volume":"3","author":"Li","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Poulose, A., and Han, D.S. (2020, January 19\u201321). Performance Analysis of Fingerprint Matching Algorithms for Indoor Localization. Proceedings of the International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan.","DOI":"10.1109\/ICAIIC48513.2020.9065220"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xia, S., Liu, Y., Yuan, G., Zhu, M., and Wang, Z. (2017). Indoor Fingerprint Positioning Based on Wi-Fi: An Overview. Int. J. Geo. Inf., 6.","DOI":"10.3390\/ijgi6050135"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Golenbiewski, J., and Tewolde, G. (2019, January 7\u20139). Implementation of an Indoor Positioning System using the WKNN Algorithm. Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2019.8666476"},{"key":"ref_19","first-page":"3925689","article-title":"An Adaptive First-Order Reliability Analysis Method for Nonlinear Problems","volume":"4","author":"Wang","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101085","DOI":"10.1016\/j.pmcj.2019.101085","article-title":"WKNN Indoor Location Algorithm based on Zone Partition by Spatial Features and Restriction of Former Location","volume":"60","author":"Yang","year":"2019","journal-title":"Pervasive Mob. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, W., Marelli, D., and Fu, M. (2021). Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering. Sensors, 21.","DOI":"10.3390\/s21041090"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"AL-Madani, B., Orujov, F., Maskeli\u016bnas, R., Dama\u0161evi\u010dius, R., and Ven\u010dkauskas, A. (2019). Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings. Sensors, 19.","DOI":"10.3390\/s19092114"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10240","DOI":"10.1109\/JSEN.2018.2875037","article-title":"Novel iBeacon Placement for Indoor Positioning in IoT","volume":"18","author":"Rezazadeh","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1109\/TII.2019.2910664","article-title":"Robust WiFi Localization by Fusing Derivative Fingerprints of RSS and Multiple Classifiers","volume":"16","author":"Guo","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Feng, Z., Cao, Y., and Yan, J. (2019, January 20\u201322). A Received Signal Strength Based Indoor Localization Algorithm Using ELM Technique and Ridge Regression. Proceedings of the 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT), Harbin, China.","DOI":"10.1109\/ICEICT.2019.8846396"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Song, B., Wang, H., Xiao, W., Huang, S., and Shi, L. (2017, January 10\u201313). Gaussian Process Model enabled Particle Filter for Device-free Localization. Proceedings of the 2017 20th International Conference on Information Fusion (Fusion), Xi\u2019an, China.","DOI":"10.23919\/ICIF.2017.8009778"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1080\/17489725.2014.977362","article-title":"A New Method for Improving Wi-Fi-based Indoor Positioning Accuracy","volume":"8","author":"Bai","year":"2014","journal-title":"J. Locat. Based Serv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, M., Zhao, L., Tan, D., and Tong, X. (2019). BLE Fingerprint Indoor Localization Algorithm Based on Eight-Neighborhood Template Matching. Sensors, 19.","DOI":"10.3390\/s19224859"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme Learning Machine: Theory and Applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","article-title":"A fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks","volume":"17","author":"Liang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","article-title":"An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels","volume":"6","author":"Huang","year":"2014","journal-title":"Cogn. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","article-title":"Trends in Extreme Learning Machines: A Review","volume":"61","author":"Huang","year":"2015","journal-title":"Neural Netw."},{"key":"ref_33","unstructured":"Seymour, L., and Lipson, M.L. (2018). Schaum\u2019s Outline of Linear Algebra, McGraw-Hill Education."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Vanderbei, R.J. (2020). The KKT System. Linear Programming, Springer Nature.","DOI":"10.1007\/978-3-030-39415-8"},{"key":"ref_35","first-page":"75","article-title":"Nonlinear Robust Regression Using Kernel Principal Component Analysis and R-Estimators","volume":"8","author":"Wibowo","year":"2011","journal-title":"Int. J. Comput. Sci. Issues"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/JSEN.2017.2660522","article-title":"Improved Wi-Fi RSSI Measurement for Indoor Localization","volume":"17","author":"Xue","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"429104","DOI":"10.1155\/2015\/429104","article-title":"Indoor Mobile Localization Based on Wi-Fi Fingerprint\u2019s Important Access Point","volume":"11","author":"Jiang","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_38","first-page":"3852","article-title":"Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System","volume":"62","author":"Yin","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5182","DOI":"10.1109\/TIE.2016.2608897","article-title":"Distributed Hybrid Particle\/FIR Filtering for Mitigating NLOS Effects in TOA-based Localization using Wireless Sensor Networks","volume":"64","author":"Pak","year":"2017","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/1\/71\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:02:43Z","timestamp":1760133763000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/1\/71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,17]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["ijgi11010071"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11010071","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,17]]}}}