{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:10:52Z","timestamp":1788394252916,"version":"build-2803163510"},"reference-count":41,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"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>Indoor positioning systems based on received signal strength (RSS) achieve indoor positioning by leveraging the position-related features inherent in spatial RSS fingerprint images. Their positioning accuracy and robustness are directly influenced by the quality of fingerprint features. However, the inherent spatial low-resolution characteristic of spatial RSS fingerprint images makes it challenging to effectively extract subtle fingerprint features. To address this issue, this paper proposes an RSS-based indoor positioning method that combines enhanced spatial frequency fingerprint representation with fusion learning. First, bicubic interpolation is applied to improve image resolution and reveal finer spatial details. Then, a 2D fast Fourier transform (2D FFT) converts the enhanced spatial images into frequency domain representations to supplement spectral features. These spatial and frequency fingerprints are used as dual-modality inputs for a parallel convolutional neural network (CNN) model with efficient multi-scale attention (EMA) modules. The model extracts modality-specific features and fuses them to generate enriched representations. Each modality\u2014spatial, frequency, and fused\u2014is passed through a dedicated fully connected network to predict 3D coordinates. A coordinate optimization strategy is introduced to select the two most reliable outputs for each axis (x, y, z), and their average is used as the final estimate. Experiments on seven public datasets show that the proposed method significantly improves positioning accuracy, reducing the mean positioning error by up to 47.1% and root mean square error (RMSE) by up to 54.4% compared with traditional and advanced time\u2013frequency methods.<\/jats:p>","DOI":"10.3390\/s25175408","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T12:04:28Z","timestamp":1756814668000},"page":"5408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Dual-Modality CNN Approach for RSS-Based Indoor Positioning Using Spatial and Frequency Fingerprints"],"prefix":"10.3390","volume":"25","author":[{"given":"Xiangchen","family":"Lai","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunzhi","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Chemical Engineering and Technology, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3165-3349","authenticated-orcid":false,"given":"Yong","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8343","DOI":"10.1109\/JIOT.2020.2989501","article-title":"Landmark Graph-Based Indoor Localization","volume":"7","author":"Gu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11183","DOI":"10.1109\/JIOT.2020.2996610","article-title":"Indoor Localization System Using Dual-Frequency Bands and Interpolation Algorithm","volume":"7","author":"Yang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1109\/MWC.023.2200633","article-title":"Indoor Positioning in 5G-Advanced: Challenges and Solution Toward Centimeter-Level Accuracy with Carrier Phase Enhancements","volume":"31","author":"Nikonowicz","year":"2024","journal-title":"IEEE Wirel. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aziz, T., and Koo, I. (2025). A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors. Appl. Sci., 15.","DOI":"10.3390\/app15031544"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Leitch, S.G., Ahmed, Q.Z., Abbas, W.B., Hafeez, M., Laziridis, P.I., Sureephong, P., and Alade, T. (2023). On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies. Sensors, 23.","DOI":"10.3390\/s23208598"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"109349","DOI":"10.1016\/j.measurement.2021.109349","article-title":"Smartphone-based indoor localization techniques: State-of-the-art and classification","volume":"179","author":"Tiglao","year":"2021","journal-title":"Measurement"},{"key":"ref_7","first-page":"775","article-title":"RADAR: An in-building RF-based user location and tracking system","volume":"Volume 2","author":"Bahl","year":"2000","journal-title":"Proceedings of the 19th Joint Conference of the IEEE Computer and Communications Societies"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1049\/iet-com.2019.1059","article-title":"Survey on WiFi-Based Indoor Positioning Techniques","volume":"14","author":"Liu","year":"2020","journal-title":"IET Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1007\/s11277-018-5862-7","article-title":"Performance Analysis of 3D Localization for a Launch Vehicle Using TOA AOA and TDOA","volume":"103","author":"Kwon","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_10","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":"2015","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Abbas, H.A., Boskany, N.W., Ghafoor, K.Z., and Rawat, D.B. (2021, January 10\u201312). Wi-Fi Based Accurate Indoor Localization System Using SVM and LSTM Algorithms. Proceedings of the 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, NV, USA.","DOI":"10.1109\/IRI51335.2021.00065"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9812625","DOI":"10.1109\/ACCESS.2022.3187837","article-title":"DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning","volume":"10","author":"Njima","year":"2022","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"32868","DOI":"10.1109\/JIOT.2024.3420122","article-title":"C2R: A Novel ANN Architecture for Boosting Indoor Positioning With Scarce Data","volume":"11","author":"Klus","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_14","first-page":"127","article-title":"SceneGraphLoc: Cross-Modality Coarse Visual Localization on 3D Scene Graphs","volume":"Volume 15066","author":"Leonardis","year":"2024","journal-title":"Computer Vision\u2014ECCV 2024"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103244","DOI":"10.1016\/j.adhoc.2023.103244","article-title":"FSTNet: Learning spatial\u2013temporal correlations from fingerprints for indoor positioning","volume":"149","author":"Ren","year":"2023","journal-title":"Ad Hoc Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105509","DOI":"10.1016\/j.engappai.2022.105509","article-title":"EA-CNN: A smart indoor 3D positioning scheme based on Wi-Fi fingerprinting and deep learning","volume":"117","author":"Alitaleshi","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, H., Cheng, L., Li, X., and Yuan, H. (2023). Neural-Network-Based Localization Method for Wi-Fi Fingerprint Indoor Localization. Sensors, 23.","DOI":"10.3390\/s23156992"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sinha, R.S., and Hwang, S.-H. (2019). Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization. Electronics, 8.","DOI":"10.3390\/electronics8090989"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"193396","DOI":"10.1109\/ACCESS.2020.3033312","article-title":"Improving Fingerprint Indoor Localization Using Convolutional Neural Networks","volume":"8","author":"Sun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","first-page":"428","article-title":"Hybrid Fingerprint Indoor Localization Method Based on ECA-CNN","volume":"61","author":"Wang","year":"2024","journal-title":"J. Comput. Res. Dev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.1109\/ACCESS.2017.2688362","article-title":"BiLoc: Bi-Modal Deep Learning for Indoor Localization With Commodity 5GHz WiFi","volume":"5","author":"Wang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qureshi, U.M., Umer, Z., and Hancke, G. (2019, January 12\u201314). Indoor Localization Using Wireless Fidelity (WiFi) and Bluetooth Low Energy (BLE) Signals. Proceedings of the 2019 IEEE 28th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Vancouver, BC, Canada.","DOI":"10.1109\/ISIE.2019.8781189"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Monica, S., and Bergenti, F. (2019). Hybrid Indoor Localization Using WiFi and UWB Technologies. Electronics, 8.","DOI":"10.3390\/electronics8030334"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tseng, C.H., and Tsaur, W.-J. (2023). FFK: Fourier-Transform Fuzzy-c-means Kalman-Filter Based RSSI Filtering Mechanism for Indoor Positioning. Sensors, 23.","DOI":"10.3390\/s23198274"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Soro, B., and Lee, C. (2019). A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization. Sensors, 19.","DOI":"10.3390\/s19081790"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104892","DOI":"10.1109\/ACCESS.2019.2932469","article-title":"Joint Time-Frequency RSSI Features for Convolutional Neural Network-Based Indoor Fingerprinting Localization","volume":"7","author":"Soro","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102167","DOI":"10.1016\/j.vlsi.2024.102167","article-title":"An energy-efficient image filtering interpolation algorithm using domain-specific dynamic reconfigurable array processor","volume":"96","author":"Guo","year":"2024","journal-title":"Integration"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2736","DOI":"10.1049\/ipr2.12228","article-title":"Detail texture detection based on Yolov4-tiny combined with attention mechanism and bicubic interpolation","volume":"15","author":"Hui","year":"2021","journal-title":"IET Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yu, M., Xu, Z.H., and Lukasiewicz, T. (2025). A general survey on medical image super-resolution via deep learning. Comput. Biol. Med., 193.","DOI":"10.1016\/j.compbiomed.2025.110345"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mendoza-Silva, M., Matey-Sanz, M., Torres-Sospedra, J., and Huerta, J. (2019). BLE RSS measurements dataset for research on accurate indoor positioning. Data, 4.","DOI":"10.3390\/data4010012"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Aranda, F.J., Parralejo, F., \u00c1lvarez, F.J., and Torres-Sospedra, J. (2020). Multi-slot BLE raw database for accurate positioning in mixed indoor\/outdoor environments. Data, 5.","DOI":"10.3390\/data5030067"},{"key":"ref_32","unstructured":"Moreira, A., Nicolau, M.J., Silva, I., Torres-Sospedra, J., Pend\u00e3o, C., and Meneses, F. (2019). Wi-Fi Fingerprinting Dataset with Multiple Simultaneous Interfaces (1.0) [Data Set], Zenodo."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"91027","DOI":"10.1109\/ACCESS.2022.3201645","article-title":"Wi-Fi and Bluetooth contact tracing without user intervention","volume":"10","author":"Yuen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., and Huang, Z. (2023, January 4\u201310). Efficient Multi-Scale Attention Module with Cross-Spatial Learning. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"ref_35","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.buildenv.2018.05.026","article-title":"Image retrieval using BIM and features from pretrained VGG network for indoor localization","volume":"140","author":"Ha","year":"2018","journal-title":"Build. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sinha, S., and Le, D.V. (December, January 29). Completely Automated CNN Architecture Design Based on VGG Blocks for Fingerprinting Localisation. Proceedings of the 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Lloret de Mar, Spain.","DOI":"10.1109\/IPIN51156.2021.9662642"},{"key":"ref_38","unstructured":"Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical Evaluation of Rectified Activations in Convolution Network. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1109\/TMC.2020.3017176","article-title":"A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting","volume":"21","author":"Richter","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4556","DOI":"10.1109\/TWC.2021.3060482","article-title":"Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems","volume":"20","author":"Wu","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1109\/JIOT.2022.3214211","article-title":"A Federated Learning Framework for Fingerprinting-Based Indoor Localization in Multibuilding and Multifloor Environments","volume":"10","author":"Gao","year":"2023","journal-title":"IEEE Internet Things J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5408\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:37:39Z","timestamp":1760035059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,2]]},"references-count":41,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25175408"],"URL":"https:\/\/doi.org\/10.3390\/s25175408","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,2]]}}}