High-Accuracy Indoor Positioning Using Deep Neural Networks Combining with Optimal Hyperparameters and Fault-Tolerant Mechanism
DOI:
https://doi.org/10.15837/ijccc.2026.2.7398Keywords:
Convolution Neural Network, indoor positioning system, Channel Impulse Response, Simulated Annealing, Hyperparameter OptimizationAbstract
High-accuracy indoor positioning is essential for reliable wireless services and drone monitoring, yet the performance remains highly sensitive to access point (AP) failures. This paper proposes a fingerprinting-based localization framework utilizing Channel Impulse Response (CIR) features and a deep convolutional neural network (DCNN) to ensure robust positioning under both nominal and degraded operational conditions. Two scenarios are examined. In the first scenario, where all four APs operate normally, the influence of network depth and hyperparameter settings on localization accuracy is systematically evaluated. The network block count is progressively reduced and optimal hyperparameters are selected via the Hyperband algorithm, followed by parameter refinement through simulated annealing in a two-stage optimization strategy. Second, in single-AP failure conditions, a mitigation approach is employed wherein a single-input DCNN is retrained for each failure case, and its hyperparameters are independently optimized using Hyperband to compensate for reduced signal diversity. Experimental validation demonstrates that the proposed system achieves an average distance error (ADE) of 0.552 m under nominal conditions, outperforming existing methods. Under single-AP failure scenarios, the framework maintains strong positioning performance, with ADE ranging from 0.776 m to 1.019 m. These results confirm the effectiveness and resilience of the proposed DCNN architecture, highlighting its suitability for reliable indoor localization in realistic and fault-prone environments.
References
Zafari, F.; Gkelias, A.; Leung, K.K. (2019). A Survey of Indoor Localization Systems and Technologies, IEEE Communications Surveys & Tutorials, 21(3), 2568-2599, 2019. https://doi.org/10.1109/COMST.2019.2911558
Nessa, A.; Adhikari, B.; Hussain, F.; Fernando, X.N. (2020). A Survey of Machine Learning for Indoor Positioning, IEEE Access, 8, 214945-214965, 2020. https://doi.org/10.1109/ACCESS.2020.3039271
Boysen, N.; De Koster, R.; Weidinger, F. (2019). Warehousing in the e-commerce era: A survey, European Journal of Operational Research, 277(2), 396-411, 2019. https://doi.org/10.1016/j.ejor.2018.08.023
Vanhie-Van Gerwen, J.; Geebelen, K.; Wan, J.; Joseph, W.; Hoebeke, J.; De Poorter, E. (2021). Indoor drone positioning: Accuracy and cost trade-off for sensor fusion, IEEE Transactions on Vehicular Technology, 71(1), 961-974, 2021. https://doi.org/10.1109/TVT.2021.3129917
He, S.; Chan, S.-H.G. (2016). Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons, IEEE Communications Surveys & Tutorials, 18(1), 466-490, 2016. https://doi.org/10.1109/COMST.2015.2464084
Shang, S.; Wang, L. (2022). Overview of WiFi fingerprinting-based indoor positioning, Iet Communications, 16(7), 725-733, 2022. https://doi.org/10.1049/cmu2.12386
Brunato, M.; Battiti, R. (2005). Statistical learning theory for location fingerprinting in wireless LANs, Computer Networks, 47(6), 825-845, 2005. https://doi.org/10.1016/j.comnet.2004.09.004
Nabati, M.; Ghorashi, S.A. (2023). A real-time fingerprint-based indoor positioning using deep learning and preceding states, Expert Systems with Applications, 213, 118889, 2023. https://doi.org/10.1016/j.eswa.2022.118889
Dao, V.-L.; Salman, S.M. (2022). Deep neural network for indoor positioning based on channel impulse response, In 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), 1-8, 2022. https://doi.org/10.1109/ETFA52439.2022.9921735
Li, L.; Jamieson, K.; DeSalvo, G.; Rostamizadeh, A.; Talwalkar, A. (2018). Hyperband: A novel bandit-based approach to hyperparameter optimization, Journal of Machine Learning Research, 18(185), 1-52, 2018.
Namee, K.; Kaewkajone, T.; Thierchot, T.; Meny, A.; Kaewsaeng-On, R.; Nimyungdee, C. (2024). Improving Indoor Navigation Accuracy with Neural Networks: A Focus on Signal Propagation Challenges, In Proceedings of the 2024 13th International Conference on Networks, Communication and Computing, 32-37, 2024. https://doi.org/10.1145/3711650.3711655
Chapre, Y.; Mohapatra, P.; Jha, S.; Seneviratne, A. (2013). Received signal strength indicator and its analysis in a typical WLAN system (short paper), In 38th Annual IEEE Conference on Local Computer Networks, 304-307, 2013. https://doi.org/10.1109/LCN.2013.6761255
Yang, Z.; Zhou, Z.; Liu, Y. (2013). From RSSI to CSI: Indoor localization via channel response, ACM Computing Surveys (CSUR), 46(2), 1-32, 2013. https://doi.org/10.1145/2543581.2543592
Zhao, W.; Han, S.; Hu, R.Q.; Meng, W.; Jia, Z. (2018). Crowdsourcing and multisource fusionbased fingerprint sensing in smartphone localization, IEEE Sensors Journal, 18(8), 3236-3247, 2018. https://doi.org/10.1109/JSEN.2018.2805335
Kjærgaard, M.B.; Munk, C.V. (2008). Hyperbolic Location Fingerprinting: A Calibration-Free Solution for Handling Differences in Signal Strength (concise contribution), In 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom), 110- 116, 2008. https://doi.org/10.1109/PERCOM.2008.75
Guo, X.; et al. (2018). Knowledge aided adaptive localization via global fusion profile, IEEE Internet Things Journal, 5(2), 1081-1089, 2018. https://doi.org/10.1109/JIOT.2017.2787594
Yu, K.; Wen, K.; Li, Y.; Zhang, S.; Zhang, K. (2019). A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments, IEEE Transactions on Vehicular Technology, 68(1), 686-699, 2019. https://doi.org/10.1109/TVT.2018.2883810
Poulose, A.; Han, D.S. (2020). UWB indoor localization using deep learning LSTM networks, Applied Sciences, 10(18), 6290, 2020. https://doi.org/10.3390/app10186290
Han, S.; Li, Y.; Meng, W.; Li, C.; Liu, T.; Zhang, Y. (2019). Indoor Localization With a Single Wi-Fi Access Point Based on OFDM-MIMO, IEEE Systems Journal, 13(1), 964-972, 2019. https://doi.org/10.1109/JSYST.2018.2823358
AlHajri, M.I.; Ali, N.T.; Shubair, R.M. (2019). Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach, IEEE Antennas and Wireless Propagation Letters, 18(11), 2306-2310, 2019. https://doi.org/10.1109/LAWP.2019.2915047
Maranò, S.; Gifford, W.M.; Wymeersch, H.; Win, M.Z. (2010). NLOS identification and mitigation for localization based on UWB experimental data, IEEE Journal on Selected Areas in Communications, 28(7), 1026-1035, 2010. https://doi.org/10.1109/JSAC.2010.100907
Rezgui, Y.; Pei, L.; Chen, X.; Wen, F.; Han, C. (2017). An efficient normalized rank based SVM for room level indoor WiFi localization with diverse devices, Mobile Information Systems, 2017, 6268797, 2017. https://doi.org/10.1155/2017/6268797
Wu, Z.; Fu, K.; Jedari, E.; Shuvra, S.R.; Rashidzadeh, R.; Saif, M. (2016). A Fast and Resource Efficient Method for Indoor Positioning Using Received Signal Strength, IEEE Transactions on Vehicular Technology, 65(12), 9747-9758, 2016. https://doi.org/10.1109/TVT.2016.2530761
Wymeersch, H.; Marano, S.; Gifford, W.M.; Win, M.Z. (2012). A Machine Learning Approach to Ranging Error Mitigation for UWB Localization, IEEE Transactions on Communications, 60(6), 1719-1728, 2012. https://doi.org/10.1109/TCOMM.2012.042712.110035
Zhu, H.; Cheng, L.; Li, X.; Yuan, H. (2023). Neural-network-based localization method for Wi-Fi fingerprint indoor localization, Sensors, 23(15), 6992, 2023. https://doi.org/10.3390/s23156992
Hoang, M.T.; Yuen, B.; Dong, X.; Lu, T.; Westendorp, R.; Reddy, K. (2019). Recurrent Neural Networks for Accurate RSSI Indoor Localization, IEEE Internet of Things Journal, 6(6), 10639- 10651, 2019. https://doi.org/10.1109/JIOT.2019.2940368
Cai, M.; Lin, Z. (2023). Precise WiFi Indoor Positioning using Deep Learning Algorithms, arXiv preprint arXiv:2307.02011, 2023.
Wang, X.; Wang, X.; Mao, S. (2020). Deep Convolutional Neural Networks for Indoor Localization with CSI Images, IEEE Transactions on Network Science and Engineering, 7(1), 316-327, 2020. https://doi.org/10.1109/TNSE.2018.2871165
Le, D.V.; Meratnia, N.; Havinga, P.J.M. (2018). Unsupervised Deep Feature Learning to Reduce the Collection of Fingerprints for Indoor Localization Using Deep Belief Networks, In 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 1-7, 2018. https://doi.org/10.1109/IPIN.2018.8533790
Liu, K.; Zhang, H.; Ng, J.K.-Y.; Xia, Y.; Feng, L.; Lee, V.C.S.; Son, S.H. (2018). Toward Low- Overhead Fingerprint-Based Indoor Localization via Transfer Learning: Design, Implementation, and Evaluation, IEEE Transactions on Industrial Informatics, 14(3), 898-908, 2018. https://doi.org/10.1109/TII.2017.2750240
Vishwakarma, R.; Joshi, R.B.; Mishra, S. (2023). IndoorGNN: A graph neural network based approach for indoor localization using WiFi RSSI, In International Conference on Big Data Analytics, Springer, 150-165, 2023. https://doi.org/10.1007/978-3-031-49601-1_11
Nguyen, D.K.; Nguyen, L.C.; Hoang, M.K. (2024). A Bayesian Optimization Based Deep Learning Model for Wi-Fi Fingerprinting based Indoor Positioning, Journal of Communications, 19(10), 2024.
Yun, Z.; Iskander, M.F. (2015). Ray Tracing for Radio Propagation Modeling: Principles and Applications, IEEE Access, 3, 1089-1100, 2015. https://doi.org/10.1109/ACCESS.2015.2453991
Liu, J.; Wang, X. (2016). Physical layer authentication enhancement using two-dimensional channel quantization, IEEE Transactions on Wireless Communications, 15(6), 4171-4182, 2016. https://doi.org/10.1109/TWC.2016.2535442
IEEE (2023). IEEE Draft Standard for Information technology-Telecommunications... (802.11be), IEEE P802.11be/D3.0, 1-999, 2023.
Li, H.; Qian, Z.; Tian, C.; Wang, X. (2020). TILoc: Improving the robustness and accuracy for fingerprint-based indoor localization, IEEE Internet of Things Journal, 7(4), 3053-3066, 2020. https://doi.org/10.1109/JIOT.2020.2964875
Bai, J.; Sun, Y.; Meng, W.; Li, C. (2021). Wi-Fi Fingerprint-Based Indoor Mobile User Localization Using Deep Learning, Wireless Communications and Mobile Computing, 2021, 6660990, 2021. https://doi.org/10.1155/2021/6660990
Karakusak, M.Z.; Kivrak, H.; Ates, H.F.; Ozdemir, M.K. (2022). RSS-based wireless LAN indoor localization and tracking using deep architectures, Big Data and Cognitive Computing, 6(3), 84, 2022. https://doi.org/10.3390/bdcc6030084
Chen, M.; Pu, Q. (2025). Bayesian optimized indoor positioning algorithm based on dual clustering, Scientific Reports, 15(1), 9272, 2025. https://doi.org/10.1038/s41598-024-79647-x
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