{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T03:18:04Z","timestamp":1761621484600,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,22]],"date-time":"2019-11-22T00:00:00Z","timestamp":1574380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772574,  61375080"],"award-info":[{"award-number":["61772574,  61375080"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Joint Funds from the National Natural Science Foundation and the Guangdong Big Data Center","award":["U1811462"],"award-info":[{"award-number":["U1811462"]}]},{"DOI":"10.13039\/501100012325","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["18ZDA308"],"award-info":[{"award-number":["18ZDA308"]}],"id":[{"id":"10.13039\/501100012325","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Radio tomographic imaging (RTI) is a technology for target localization by using radio frequency (RF) sensors in a wireless network. The change of the attenuation field caused by the target is represented by a shadowing image, which is then used to estimate the target\u2019s position. The shadowing image can be reconstructed from the variation of the received signal strength (RSS) in the wireless network. However, due to the interference from multi-path fading, not all the RSS variations are reliable. If the unreliable RSS variations are used for image reconstruction, some artifacts will appear in the shadowing image, which may cause the target\u2019s position being wrongly estimated. Due to the sparse property of the shadowing image, sparse Bayesian learning (SBL) can be employed for signal reconstruction. Aiming at enhancing the robustness to multipath fading, this paper explores the Laplace prior to characterize the shadowing image under the framework of SBL. Bayesian modeling, Bayesian inference and the fast algorithm are presented to achieve the maximum-a-posterior (MAP) solution. Finally, imaging, localization and tracking experiments from three different scenarios are conducted to validate the robustness to multipath fading. Meanwhile, the improved computational efficiency of using Laplace prior is validated in the localization-time experiment as well.<\/jats:p>","DOI":"10.3390\/s19235126","type":"journal-article","created":{"date-parts":[[2019,11,22]],"date-time":"2019-11-22T10:42:40Z","timestamp":1574419360000},"page":"5126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Exploring the Laplace Prior in Radio Tomographic Imaging with Sparse Bayesian Learning towards the Robustness to Multipath Fading"],"prefix":"10.3390","volume":"19","author":[{"given":"Zhen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Sun Yat-sen University, Guangzhou 510006, China"}]},{"given":"Xuemei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China"},{"name":"Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4031-7996","authenticated-orcid":false,"given":"Guoli","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China"},{"name":"Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou 510006, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1109\/TMC.2009.174","article-title":"Radio tomographic imaging with wireless networks","volume":"9","author":"Joey","year":"2010","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_2","unstructured":"Dustin, M., Joey, W., and Neal, P. (2013, January 21\u201324). Toward a rapidly deployable radio tomographic imaging system for tactical operations. Proceedings of the 38th Annual IEEE Conference on Local Computer Networks, Sydney, NSW, Australia."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1109\/TMC.2015.2504965","article-title":"RTI goes wild: Radio tomographic imaging for outdoor people detection and localization","volume":"15","author":"Cesare","year":"2016","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/JSTSP.2013.2286774","article-title":"Radio tomography for roadside surveillance","volume":"8","author":"Christopher","year":"2014","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_5","unstructured":"Ossi, K., Maurizio, B., and Neal, P. (2012, January 22\u201325). Follow@ grandma: Long-term device-free localization for residential monitoring. Proceedings of the 37th Annual IEEE Conference on Local Computer Networks, Clearwater, FL, USA."},{"key":"ref_6","first-page":"445","article-title":"EasyTrack: Zero-Calibration Smart-Home Tracking System","volume":"27","author":"Nathavuth","year":"2019","journal-title":"J. Inf. Process. Syst."},{"key":"ref_7","unstructured":"Maurizio, B., Ossi, K., and Neal, P. (2013). Radio Tomographic Imaging for Ambient Assisted Living. Evaluating AAL Systems Through Competitive Benchmarking, Springer."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.3390\/ijerph13111152","article-title":"An Indoor Monitoring System for Ambient Assisted Living Based on Internet of Things Architecture","volume":"13","author":"Goncalo","year":"2016","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_9","unstructured":"Millar, G., Aghdasi, F., and Lei, W. (2017). Tracking Moving Objects using a Camera Network. (9615064), U.S. Patent."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1109\/TIM.2016.2534319","article-title":"Maximizing Localization Accuracy via Self-Configurable Ultrasonic Sensor Grouping Using Genetic Approach","volume":"65","author":"Taejoon","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7619","DOI":"10.1109\/JSEN.2018.2862412","article-title":"An Accurate Geometrical Multi-Target Device-Free Localization Method Using Light Sensors","volume":"18","author":"Zhang","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_12","unstructured":"Jurgen, K., and Daniel, H. (2010, January 11\u201312). Passive infrared localization with a Probability Hypothesis Density filter. Proceedings of the 7th Workshop on Positioning, Navigation and Communication, Dresden, Germany."},{"key":"ref_13","unstructured":"Sujay, N., Sujay, N., Vijay, S.R., Prabhakar, T.V., Sripad, S.K., and Madhuri, S.I. (2015). PIR sensors: Characterization and novel localization technique. IPSN \u201915 Proceedings of the 14th International Conference on Information Processing in Sensor Networks, ACM Digital Library."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/TMC.2010.175","article-title":"See-through walls: Motion tracking using variance-based radio tomography networks","volume":"10","author":"Wilson","year":"2011","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_15","first-page":"763","article-title":"CSI-based fingerprinting for indoor localization: A deep learning approach","volume":"66","author":"Wang","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/TPDS.2012.214","article-title":"CSI-based indoor localization","volume":"24","author":"Wu","year":"2013","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1109\/JPROC.2010.2052010","article-title":"RF Sensor Networks for Device-Free Localization: Measurements, Models, and Algorithms","volume":"98","author":"Neal","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_18","unstructured":"Henri, N., Anssi, R., Simo, A.L., and Robert, P. (2014, January 28\u201331). Particle filter and smoother for indoor localization. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Montbeliard-Belfort, France."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1109\/THMS.2016.2611826","article-title":"Accurate and reliable human localization using composite particle\/FIR filtering","volume":"47","author":"Min","year":"2017","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, J.B., Yang, F., Niu, X.G., Li, L.L., Wang, Z.M., and Chen, R.Z. (2018). A Bayesian Density Model Based Radio Signal Fingerprinting Positioning Method for Enhanced Usability. Sensors, 18.","DOI":"10.3390\/s18114063"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1109\/TIM.2018.2819378","article-title":"A Performance Study of a Fast-Rate WLAN Fingerprint Measurement Collection Method","volume":"67","author":"Erick","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1109\/TII.2015.2433211","article-title":"A geometric filter algorithm for robust device-free localization in wireless networks","volume":"12","author":"Talampas","year":"2016","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xiao, W.D., Zhang, S., and Huang, S.D. (2017). Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction. Sensors, 17.","DOI":"10.3390\/s17040879"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7302","DOI":"10.1109\/TVT.2017.2664938","article-title":"ARTI: An Adaptive Radio Tomographic Imaging System","volume":"66","author":"Kaltiokallio","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_25","first-page":"58","article-title":"Detector Based Radio Tomographic Imaging","volume":"17","author":"Huseyin","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Z., Su, H., Guo, X.M., and Wang, G.L. (July, January 30). Radio Tomographic Imaging with Feedback-Based Sparse Bayesian Learning. Proceedings of the 2018 Eighth International Conference on Information Science and Technology (ICIST), Cordoba, Spain.","DOI":"10.1109\/ICIST.2018.8426185"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1109\/TSP.2018.2799169","article-title":"Blind Radio Tomography","volume":"66","author":"Daniel","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_28","unstructured":"Yigitler, H. (2018). Narrowband Radio Frequency Inference: Physical Modeling and Measurement Processing. [Ph.D. Thesis, Aalto University]."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yigitler, H., Ossi, K., and Riku, J. (2018). Received Signal Strength Models for Narrowband Radios, IGI Global. Chapter 2.","DOI":"10.4018\/978-1-5225-3528-7.ch002"},{"key":"ref_30","unstructured":"Jakub, N., Zdenek, T., Vlastimil, B., Ladislav, P., Ondrej, K., Libor, B., Jiri, S., and Tomas, K. (2016, January 19\u201320). Study of the performance of RSSI based Bluetooth Smart indoor positioning. Proceedings of the 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA), Kosice, Slovakia."},{"key":"ref_31","unstructured":"Bo, W., Ambuj, V., Neal, P., Wen, H., Thiemo, V., and Chou, C.T. (2015, January 13\u201316). dRTI: Directional radio tomographic imaging. Proceedings of the 14th International Conference on Information Processing in Sensor Networks, Seattle, WA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/JRFID.2017.2750018","article-title":"On-Wall, Wide Bandwidth E-Shaped Patch Antenna for Improved Whole-Home Radio Tomography","volume":"1","author":"Cheng","year":"2017","journal-title":"IEEE J. Radio Freq. Identif. (RFID)"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, S.X., Liu, H., Gao, F., and Wang, Z.H. (2019). Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity. Sensors, 19.","DOI":"10.3390\/s19030439"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kaltiokallio, O., Bocca, M., and Patwari, N. (2012, January 8\u201311). Enhancing the accuracy of radio tomographic imaging using channel diversity. Proceedings of the 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012), Las Vegas, NV, USA.","DOI":"10.1109\/MASS.2012.6502524"},{"key":"ref_35","unstructured":"Stijn, D., Rafael, B., Glenn, E., and Maarten, W. (2017, January 18\u201321). Multi-frequency sub-1 GHz radio tomographic imaging in a complex indoor environment. Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jin, J., Ke, W., Lu, J., Wang, Y.L., and Zoran, S. (2018, January 22\u201323). Multi-channel RTI fusion based on improved joint sparse model. Proceedings of the 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), Wuhan, China.","DOI":"10.1109\/UPINLBS.2018.8559703"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/TVT.2014.2318084","article-title":"Device-free localization with multidimensional wireless link information","volume":"64","author":"Wang","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/TMC.2011.102","article-title":"A fade-level skew-laplace signal strength model for device-free localization with wireless networks","volume":"11","author":"Wilson","year":"2012","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_39","unstructured":"Yang, L.W., Huang, K.D., Wang, G.L., and Guo, X.M. (July, January 29). An enhanced multi-scale model for shadow fading in radio tomographic imaging. Proceedings of the 11th World Congress on Intelligent Control and Automation, Shenyang, China."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tan, J.J., Zhao, X., Yang, L.W., Guo, X.M., and Wang, G.L. (2018, January 19\u201323). Backprojection and Integration for the Multi-Scale Spatial Model in Radio Tomographic Imaging. Proceedings of the 2018 IEEE 8th Annual International Conference on CYBER Technology in Autoumation, Control, and Intelligent System (CYBER), Tianjin, China.","DOI":"10.1109\/CYBER.2018.8688136"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"50223","DOI":"10.1109\/ACCESS.2019.2910607","article-title":"Radio Tomographic Imaging Based on Low-Rank and Sparse Decomposition","volume":"7","author":"Tan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1049\/iet-spr.2013.0501","article-title":"Heterogeneous Bayesian compressive sensing for sparse signal recovery","volume":"8","author":"Huang","year":"2014","journal-title":"IET Signal Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.pmcj.2017.03.001","article-title":"Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing","volume":"40","author":"Huang","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2386","DOI":"10.1109\/TMC.2012.206","article-title":"Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks","volume":"12","author":"Andrea","year":"2013","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2010RS004561","article-title":"Localization, tracking, and imaging of targets in wireless sensor networks: An invited review","volume":"46","author":"Viani","year":"2011","journal-title":"Radio Sci."},{"key":"ref_46","first-page":"211","article-title":"Sparse Bayesian learning and the relevance vector machine","volume":"1","author":"Tipping","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, Z., Qin, L., Guo, X.M., and Wang, G.L. (2019). Dual radio tomographic imaging with shadowing-measurement awareness. IEEE Trans. Instrum. Meas.","DOI":"10.1109\/TIM.2019.2942171"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TIP.2009.2032894","article-title":"Bayesian Compressive Sensing Using Laplace Priors","volume":"19","author":"Derin","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","unstructured":"Michael, E.T., and Anita, F. (2003, January 26). Fast Marginal Likelihood Maximisation for Sparse Bayesian Models. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics (AISTATS), Cambridge, UK."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2346","DOI":"10.1109\/TSP.2007.914345","article-title":"Bayesian Compressive Sensing","volume":"56","author":"Ji","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Figueiredo, M. (2001, January 3\u20138). Adaptive sparseness using Jeffreys prior. Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, Vancouver, BC, Canada.","DOI":"10.7551\/mitpress\/1120.003.0094"},{"key":"ref_52","unstructured":"Kun, M. (2019, November 17). Jeffreys Prior: Philosophy, Information Geometry and Empirical Bayesian Methods. Available online: https:\/\/www.researchgate.net\/publication\/323736141_Jeffreys_Prior_Philosophy_Information_Geometry_and_Empirical_Bayesian_Methods."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5126\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:36:52Z","timestamp":1760189812000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,22]]},"references-count":52,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235126"],"URL":"https:\/\/doi.org\/10.3390\/s19235126","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,11,22]]}}}