SWiBluX: Multi-Sensor Deep Learning Fingerprint for Precise Real-Time Indoor Tracking
Open accessAbstract
Indoor/outdoor localization topic has gained a significant research interest due to the wide range of potential applications. Commonly, the Fingerprinting methods for spatial characterization of the environments monitored are employed in deterministic/statistical estimation. However, there are Fingerprint parameters that are generally neglected and can seriously affect the performance yielding to low accurate location. Nowadays, machine and deep learning (DL) methods are employed in this topic due to its ability to approximate complex non-linear models being capable of mitigating the undesirable effects of wireless propagation. In this paper, a complete overview of most influential aspects in Fingerprinting and indoor tracking methods is presented. Furthermore, a novel multi-modal complete tracking system, called SWiBluX, based on statistic and DL techniques is presented. The system relies on relevant feature extraction from available data sources to estimate user's/target indoor position using a multi-phase statistical Fingerprint and DL disruptive approach. In addition, a Gaussian outlier filter is applied to the position estimation model output to further reduce the error in the estimation. The set of experiments performed shows that Fingerprint positioning accuracy estimation can be improved up to 45% resulting in a final estimation error that outperforms related literature.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Deep learning techniques can greatly improve the accuracy of indoor positioning.
“the accuracy of indoor positioning can be greatly improved by deep learning techniques [72]”
From What should 6G be? (2020) · cites this paper as [72]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531 · see citing works
(2011) · Cited by 5,203 · see citing works
Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches
Simon, D. (2006) · doi:10.1002/0470045345 · Cited by 4,875 · see citing works
Nonlinear Complementary Filters on the Special Orthogonal Group
Mahony, R. E., Hamel, T., & Pflimlin, J.-M. (2008) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.923738 · Cited by 1,765 · see citing works
The indoor radio propagation channel
Hashemi, H. (1993) · Proceedings of the IEEE · doi:10.1109/5.231342 · Cited by 1,739 · see citing works
Machine learning with big data: challenges and approaches.
L'Heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. M. (2017) · IEEE Access · doi:10.1109/access.2017.2696365 · Cited by 1,023 · see citing works
Torres-Sospedra, J., Montoliu, R., Martinez-Uso, A., Avariento, J. P., Arnau, T. J., Benedito-Bordonau, M., & Huerta, J. (2014) · 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN) · doi:10.1109/ipin.2014.7275492 · Cited by 615 · see citing works
Resampling Methods for Particle Filtering: Classification, implementation, and strategies
Li, T., Bolić, M., & Djurić, P. M. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2330626 · Cited by 605 · see citing works
Properties of indoor received signal strength for WLAN location fingerprinting
Kaemarungsi, K., & Krishnamurthy, P. (2004) · The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004. · doi:10.1109/mobiq.2004.1331706 · Cited by 498 · see citing works
CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach
Wang, X., Gao, L., & Mao, S. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2558659 · Cited by 431 · see citing works
DeepFi: Deep learning for indoor fingerprinting using channel state information
Wang, X., Gao, L., Mao, S., & Pandey, S. (2015) · 2015 IEEE Wireless Communications and Networking Conference (WCNC) · doi:10.1109/wcnc.2015.7127718 · Cited by 381 · see citing works
A Comparison of Complementary and Kalman Filtering
Higgins, W. T. (1975) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.1975.308081 · Cited by 301 · see citing works
A review of deep learning in image recognition
Pak, M., & Kim, S.-H. (2017) · International Conference on Computer Applications and Information Processing Technology (CAIPT) · doi:10.1109/caipt.2017.8320684 · Cited by 252 · see citing works
Comprehensive Review On Supervised Machine Learning Algorithms
Choudhary, R., & Gianey, H. K. (2017) · doi:10.1109/mlds.2017.11 · Cited by 216 · see citing works
A Multi-Mode Dead Reckoning System for Pedestrian Tracking Using Smartphones
Tian, Q., Salcic, Z. A., Wang, K. I., & Pan, Y. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2510364 · Cited by 185 · see citing works
Evaluation of the reliability of RSSI for indoor localization
Dong, Q., & Dargie, W. (2012) · doi:10.1109/icwcuca.2012.6402492 · Cited by 144 · see citing works
EM ENERGY ABSORPTION IN THE HUMAN BODY TISSUES DUE TO UWB ANTENNAS
Klemm, M., & Troester, G. (2006) · Electromagnetic waves · doi:10.2528/pier06040601 · Cited by 123 · see citing works
On Calibration of Three-Axis Magnetometer
Wu, Y., & Shi, W. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2459767 · Cited by 108 · see citing works
Farshad, A., Li, J., Marina, M. K., & Garcia, F. J. (2013) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2013.6817920 · Cited by 97 · see citing works
Performance Analysis of RSS Fingerprinting Based Indoor Localization
Tian, X., Shen, R., Liu, D., Wen, Y., & Wang, X. (2016) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2016.2645221 · Cited by 96 · see citing works
Mobile Phone-Based Displacement Estimation for Opportunistic Localisation Systems
Bylemans, I., Weyn, M., & Klepal, M. (2009) · International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM) · doi:10.1109/ubicomm.2009.23 · Cited by 85 · see citing works
Tao, G., Archambault, P. S., & Levin, M. F. (2013) · International Conference on Virtual Rehabilitation · doi:10.1109/icvr.2013.6662084 · Cited by 76 · see citing works
Estimation of Step Length and Gait Asymmetry Using Wearable Inertial Sensors
Wang, L., Sun, Y. T., Li, Q., & Liu, T. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2815700 · Cited by 65 · see citing works
Ahmadi, H., & Bouallègue, R. (2017) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2017.7986447 · Cited by 59 · see citing works
A review on advances in deep learning
Soniya, S., Paul, S., & Singh, L. (2015) · IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI) · doi:10.1109/wci.2015.7495514 · Cited by 58 · see citing works
MPiLoc: Self-Calibrating Multi-Floor Indoor Localization Exploiting Participatory Sensing
Luo, C., Hong, H., Chan, M. C., Li, J., Zhang, X., & Ming, Z. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2698453 · Cited by 58 · see citing works
Indoor human localization with orientation using WiFi fingerprinting
Husen, M. N., & Lee, S. (2014) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1145/2557977.2557980 · Cited by 52 · see citing works
Smartphone-Based Indoor Localization System Using Inertial Sensor and Acoustic Transmitter/Receiver
Yang, H. T., Zhang, R., Bordoy, J., Höflinger, F., Li, W., Schindelhauer, C., & Reindl, L. M. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2604424 · Cited by 52 · see citing works
Wi-Fi Localization Using RSSI Fingerprinting
Quan, M., Navarro, E. A. V., & Peuker, B. (2010) · DigitalCommons - CalPoly (California State Polytechnic University) · Cited by 50 · see citing works
A Fast Calibration Method for Triaxial Magnetometers
Hoseinitabatabaei, S. A., Gluhak, A., & Tafazolli, R. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2263913 · Cited by 49 · see citing works
Fingerprinting localization based on neural networks and ultra-wideband signals
Yu, L., Laaraiedh, M., Avrillon, S., & Uguen, B. (2011) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2011.6151557 · Cited by 42 · see citing works
CRIL: An Efficient Online Adaptive Indoor Localization System
Cai, S., Liao, W., Luo, C., Li, M., Huang, X., & Li, P. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2597303 · Cited by 42 · see citing works
Improve Accuracy of Fingerprinting Localization with Temporal Correlation of the RSS
Tian, X., Wang, M., Li, W., Jiang, B., Xu, D., Wang, X., & Xu, J. J. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2703892 · Cited by 37 · see citing works
Optimization of Fingerprints Reporting Strategy for WLAN Indoor Localization
Tian, X., Li, W., Yang, Y., Zhang, Z., & Wang, X. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2715820 · Cited by 37 · see citing works
Maxlifd: Joint Maximum Likelihood Localization Fusing Fingerprints and Mutual Distances
He, S., Chan, S.-H. G., Yu, L., & Liu, N. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2841842 · Cited by 37 · see citing works
Deep learning for weights training and indoor positioning using multi-sensor fingerprint
Gan, X., Yu, B., Huang, L. H., & Li, Y. (2017) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2017.8115923 · Cited by 36 · see citing works
Zhu, J. Y., Zheng, A. X., Xu, J., & Li, V. O. K. (2014) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2014.7275543 · Cited by 29 · see citing works
Particle filters and resampling techniques: Importance in computational complexity analysis
Sileshi, B. G., Ferrer, C., & Oliver, J. (2013) · Conference on Design and Architectures for Signal and Image Processing · Cited by 25 · see citing works
Fine-grained indoor tracking by fusing inertial sensor and physical layer information in WLANs
Li, Z., Burbano, D., Zhao, Z., Carrera, J. L., & Braun, T. I. (2016) · International Conference on Communications · doi:10.1109/icc.2016.7511162 · Cited by 25 · see citing works
Locating user equipments and access points using RSSI fingerprints: A Gaussian process approach
Yiu, S., Dashti, M., Claußen, H., & Pérez‐Cruz, F. (2016) · International Conference on Communications · doi:10.1109/icc.2016.7511152 · Cited by 19 · see citing works
Position and velocity estimation through acceleration measurements
Estrada, A., Efimov, D. V., & Perruquetti, W. (2014) · IFAC Proceedings Volumes · doi:10.3182/20140824-6-za-1003.01211 · Cited by 15 · see citing works
Scaled Unscented Kalman Filter for RSSI-based Indoor Positioning and Tracking
Khalil, L., & Jung, P. (2015) · International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST) · doi:10.1109/ngmast.2015.20 · Cited by 12 · see citing works
Fusion system based on WiFi and ultrasounds for in-home positioning systems: The UTOPIA experiment
Sosa-Sesma, S., & Pérez-Navarro, A. (2016) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2016.7743622 · Cited by 12 · see citing works
WiFi based indoor localization with adaptive motion model using smartphone motion sensors
He, X., Li, J., & Aloi, D. N. (2014) · International Conference on Connected Vehicles and Expo · doi:10.1109/iccve.2014.7297659 · Cited by 11 · see citing works
Alternative effective sample size measures for importance sampling
Martino, L., Elvira, V., & Louzada, F. (2016) · doi:10.1109/ssp.2016.7551765 · Cited by 11 · see citing works
An experimental investigation of indoor localization by unsupervised Wi-Fi signal clustering
Xu, Y., Lau, S. L., Kusber, R., & David, K. (2012) · Future Network & Mobile Summit · Cited by 10 · see citing works
A fast indoor tracking algorithm based on particle filter and improved fingerprinting
Li, N., Chen, J., Yan, Y., & Song, C. (2016) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2016.7554206 · Cited by 10 · see citing works
Actuating Sensor For Determining The Direction Of Arrival Using Maximal RSSI
Singh, M., & Khilar, P. M. (2014) · Cited by 7 · see citing works
WMSP: Bringing the Wisdom of the Crowd to WiFi Networks
Molina, L., Kerdoncuff, T., Shehadeh, D., Montavont, N., & Blanc, A. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2694422 · Cited by 7 · see citing works
Indoor positioning WLAN based fingerprinting as supervised machine learning problem
Năstac, I., Iftimie, F. A., Arsene, O., Ilian, V. L. M., & Cramariuc, B. (2017) · IEEE International Symposium for Design and Technology in Electronic Packaging (SIITME) · doi:10.1109/siitme.2017.8259888 · Cited by 7 · see citing works
Indoor Wi-Fi tracking system using fingerprinting and Kalman filter
Mohd, B. J., Amro, I., & Alhasani, A. (2017) · IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies · doi:10.1109/aeect.2017.8257770 · Cited by 4 · see citing works
Wearable gait logging system attached on ankles to estimate foot steps and trajectories
Kato, Y., Nagano, H., Konyo, M., & Tadokoro, S. (2017) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2017.8279228 · Cited by 3 · see citing works
A New Method for Yielding a Database of Hybrid Location Fingerprints
Li, Y., Tang, W., Wang, S., & Peng, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-42294-7_18 · Cited by 2 · see citing works
Flagged first, then by citations
2 of the 56 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- IEEE 802.11
- S. Madgwick, "An efficient orientation filter for inertial and iner- tial/magnetic sensor arrays," Dept. Mech. Eng., Univ. Bristol, Bristol, U.K., Tech. Rep., 2010, vol. 25.
- G. Panchal and M. Panchal, "Review on methods of selecting number of hidden nodes in artificial neural network," Int. J. Comput. Sci. Mobile Comput., vol. 3, no. 11, pp. 455-464, 2014.
Cited by
Sorted by citations
Dang, S., Amin, O., Shihada, B., & Alouini, M.-S. (2020) · Nature Electronics · doi:10.1038/s41928-019-0355-6 · Cited by 1,790 · see citing works
Alsharif, M. H., Kelechi, A. H., Albreem, M. A. M., Chaudhry, S. A., Zia, M. S., & Kim, S. (2020) · Symmetry · doi:10.3390/sym12040676 · Cited by 361 · see citing works
Zhuang, Y., Sun, X. L., Li, Y., Huai, J., Hua, L., Yang, X., Cao, X., Zhang, P., Cao, Y., Qi, L., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.01.025 · Cited by 271 · see citing works
Smartphone-Based Indoor Positioning Using BLE iBeacon and Reliable Lightweight Fingerprint Map
Dinh, T.-M. T., Duong, N.-S., & Sandrasegaran, K. S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2989411 · Cited by 147 · see citing works
CSI-Fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network
Zhang, B., Sifaou, H., & Li, G. Y. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3235449 · Cited by 128 · see citing works
Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an Outlook
Trevlakis, S. E., Boulogeorgos, A. A., Pliatsios, D., Querol, J., Ntontin, K., Sarigiannidis, P. G., Chatzinotas, S., & Renzo, M. D. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3324952 · Cited by 114 · see citing works
On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies
Leitch, S. G., Ahmed, Q. Z., Abbas, W. B., Hafeez, M., Lazaridis, P. I., Sureephong, P., & Alade, T. T. (2023) · Sensors · doi:10.3390/s23208598 · Cited by 105 · see citing works
Deep learning methods for fingerprint-based indoor positioning: a review
Alhomayani, F. M., & Mahoor, M. H. (2020) · Journal of Location Based Services · doi:10.1080/17489725.2020.1817582 · Cited by 102 · see citing works
A Data-Driven Inertial Navigation/Bluetooth Fusion Algorithm for Indoor Localization
Chen, J., Zhou, B., Bao, S., Liu, X., Gu, Z., Li, L., Zhao, Y., Zhu, J., & Li, Q. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3089516 · Cited by 100 · see citing works
A survey of deep learning approaches for WiFi-based indoor positioning
Feng, X., Nguyen, K. A., & Luo, Z. (2021) · Journal of Information and Telecommunication · doi:10.1080/24751839.2021.1975425 · Cited by 81 · see citing works
Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network
Liu, Z., Dai, B., Wan, X., & Li, X. (2019) · Sensors · doi:10.3390/s19204597 · Cited by 47 · see citing works
An Overview of Indoor Localization Technologies: Toward IoT Navigation Services
Billa, A., Shayea, I. A. M., Alhammadi, A., Abdullah, Q., & Roslee, M. (2020) · IEEE International Symposium on Telecommunication Technologies · doi:10.1109/istt50966.2020.9279369 · Cited by 43 · see citing works
Recurrent Model for Wireless Indoor Tracking and Positioning Recovering Using Generative Networks
Belmonte-Hernández, A., Hernández-Peñaloza, G., Gutierrez, D. M., & Álvarez, F. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2958201 · Cited by 37 · see citing works
A Comprehensive Survey of Machine Learning Based Localization with Wireless SignalsPreprint
Burghal, D., Ravi, A. T., Rao, V. V., Alghafis, A., & Molisch, A. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11171 · Cited by 37 · see citing works
Talla-Chumpitaz, R., Castillo‐Cara, M., Orozco–Barbosa, L., & García‐Castro, R. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.10.011 · Cited by 35 · see citing works
New Artificial Intelligence Approach to Inclination Measurement Based on MEMS Accelerometer
Hoang, M. L., & Pietrosanto, A. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3105494 · Cited by 28 · see citing works
Babalola, O. P., & Balyan, V. (2021) · Sensors · doi:10.3390/s21206778 · Cited by 25 · see citing works
6G Wireless Communication Networks
Haque, M. A., Ahmad, S., Abboud, A. J., Hossain, M. A., Kumar, K., Haque, S., Sonal, D., Rahman, M., & Senapathy, M. (2024) · International Journal of Business Data Communications and Networking · doi:10.4018/ijbdcn.339889 · Cited by 22 · see citing works
Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering
Wang, W., Marelli, D., & Fu, M. (2021) · Sensors · doi:10.3390/s21041090 · Cited by 22 · see citing works
Sun, H., Zhu, X., Liu, Y., & Liu, W. (2020) · Sensors · doi:10.3390/s20102981 · Cited by 20 · see citing works
Huang, S., Zhao, K., Zheng, Z., Ji, W., Li, T., & Liao, X. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/9936706 · Cited by 20 · see citing works
Deep Learning for Fingerprint-Based Outdoor Positioning via LTE Networks
Li, D., & Lei, Y. (2019) · Sensors · doi:10.3390/s19235180 · Cited by 19 · see citing works
Large-Scale Deep Learning Framework on FPGA for Fingerprint-Based Indoor Localization
Liu, C., Wang, C., & Luo, J. (2020) · IEEE Access · doi:10.1109/access.2020.2985162 · Cited by 19 · see citing works
Azaddel, M. H., Nourian, M. A., ShahHosseini, K., Junoh, S. A., & Akbari, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100967 · Cited by 18 · see citing works
Orientation-Aided Stochastic Magnetic Matching for Indoor Localization
Hou, L., Li, Y., Zhuang, Y., Zhou, B., Tsai, G.-J., Luo, Y., & El‐Sheimy, N. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2945681 · Cited by 18 · see citing works
A Systematic Review of Location Aware Schemes in the Internet of Things
Khan, M. A., Saboor, A., Kim, H., & Park, H. (2021) · Sensors · doi:10.3390/s21093228 · Cited by 18 · see citing works
Yoo, J., & Park, J. (2019) · Applied Sciences · doi:10.3390/app9183930 · Cited by 17 · see citing works
RSSI-KNN: A RSSI Indoor Localization Approach with KNN
Zheng, X., Cheng, R., & Wang, Y. (2023) · IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) · doi:10.1109/eebda56825.2023.10090664 · Cited by 16 · see citing works
AI-Driven Wireless Positioning: Fundamentals, Standards, State-of-the-Art, and Challenges
Pan, G., Gao, Y., Gao, Y., Yu, W., Zhong, Z., Yang, X., Guo, X., & Xu, S. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3648577 · Cited by 23 · see citing works
Hao, Z., Qu, N., Dang, X., & Hou, J. (2019) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147719869877 · Cited by 13 · see citing works
Gao, K., Wang, H., Nazarko, J., & Chobanov, G. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3058141 · Cited by 13 · see citing works
Indoor Localization with lightweight RSS Fingerprint using BLE iBeacon on iOS platform
Duong, N.-S., & Dinh, T.-M. T. (2019) · International Symposium on Communications and Information Technologies · doi:10.1109/iscit.2019.8905160 · Cited by 11 · see citing works
Hu, G., Feldhaus, P., Feng, Y., Wang, S., Zheng, J., Duan, H., & Gu, J. (2019) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001420580045 · Cited by 11 · see citing works
Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
Yoon, J.-H., Kim, H.-J., & Kwon, S.-K. (2024) · Applied Sciences · doi:10.3390/app14083319 · Cited by 7 · see citing works
3D indoor area recognition for personnel security using integrated UWB and barometer approach
Yang, F., Liu, D., Gong, X., Chen, R., & Hyyppa, J. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-69927-x · Cited by 6 · see citing works
Richard, J., Bertet, K., & Faucher, C. (2021) · Applied Sciences · doi:10.3390/app11136107 · Cited by 5 · see citing works
A High-Precision Indoor Positioning Scheme Based on Feature Code and Lighting Device
Yang, J., Feng, L., He, P., Chen, W., Yang, A., & Lu, J. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3176678 · Cited by 5 · see citing works
A 6G network survey: state-of-the-art, issues, and future technologies
Lingaraj, K., Malghan, R. L., Rao, M. C., & Garg, L. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ade02b · Cited by 6 · see citing works
Optimal fusion‐based localization method for tracking of smartphone user in tall complex buildings
Jamil, H., & Kim, D. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12262 · Cited by 4 · see citing works
Ng, T. J., Aridas, N. K., & Othman, M. (2024) · IEEE Access · doi:10.1109/access.2024.3487901 · Cited by 4 · see citing works
He, C., Wang, X., Jiao, J., Huang, Y., Han, C., Zhang, Y., & Zhu, J. (2024) · Urban Lifeline · doi:10.1007/s44285-024-00021-4 · Cited by 3 · see citing works
IODnet: Indoor/Outdoor Telecommunication Signal Detection through Deep Neural Network
Abdollahi, M., Mashhadi, S., Sabzalizadeh, R., Mirzaei, A., Elahi, M. M., Baharloo, M. M., & Baniasadi, A. (2023) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc60832.2023.00028 · Cited by 2 · see citing works
Wireless Positioning Using Deep Learning with Data Augmentation Technique
Tian, K., Song, S., Xu, W., Li, D., & Yang, K. (2021) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc50174.2021.9569265 · Cited by 2 · see citing works
A Feature Extration Method based on Bi-Tower for Indoor Positioning
Lin, L., Yang, L., Dong, W., Yang, S., & Yu, B. (2022) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics55264.2022.9873709 · Cited by 1 · see citing works
Improving Wi-Fi indoor positioning based on matching visibility with virtual simulation
Lyu, Z., Li, L., Meng, Q., & Zhang, G. (2024) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin62893.2024.10786179 · Cited by 1 · see citing works
Toward 6G wireless communications: Vision, applications, and technologies
Alzamil, A. A. (2020) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2021.01.004 · Cited by 1 · see citing works
Yin, X., Ma, L., Sun, P., & Tan, X. (2021) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-021-00795-7 · Cited by 1 · see citing works
A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning
Pichaimani, V., & Manjula, K. R. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.023105 · Cited by 1 · see citing works
Evaluation of Sixth Generation 6G Wireless Networks
Kumar, S. S., Leelavathi, M., K., M. K. P., & Palanisamy, P. (2022) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-7998-9636-4.ch001
A Signal-Physical Siamese neural network for Wi-Fi Fingerprint localization
Wei, L., Qi, T., Ouyang, G., & Wang, B. (2022) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/euc57774.2022.00011
Intelligent Indoor Positioning Using a TestbedPreprint
Singh, S., Gupta, S., & Kumar, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4851025
- Authors
- Belmonte-Hernandez, A., Hernandez-Penaloza, G., Martin Gutierrez, D., & Alvarez, F.
- Venue
- IEEE Sensors Journal
- Published
- 2019-05-01
- Volume
- 19
- Issue
- 9
- Pages
- 3473-3486
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W2910423618
- Cited by
- 53
- References
- 56
- Citations by year
- 2025: 2 · 2024: 7 · 2023: 8 · 2022: 6 · 2021: 11 · 2020: 10 · 2019: 9
- FWCI
- 3.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Sensors Journal (publishedVersion)
- Hispana (acceptedVersion)
- Archivo Digital UPM (Universidad Politécnica de Madrid) (submittedVersion)
- Funders
- European Project: ICT4Life within the H2020 Research and Innovation Program (690090)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- indoor positioning, fingerprinting, indoor tracking, multi-sensor fusion, deep learning, positioning accuracy, feature extraction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
