{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T11:53:51Z","timestamp":1773402831606,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T00:00:00Z","timestamp":1555459200000},"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":["61871400"],"award-info":[{"award-number":["61871400"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571463"],"award-info":[{"award-number":["61571463"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20171401"],"award-info":[{"award-number":["BK20171401"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.<\/jats:p>","DOI":"10.3390\/s19081828","type":"journal-article","created":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T07:58:09Z","timestamp":1555487889000},"page":"1828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7523-1864","authenticated-orcid":false,"given":"Dongping","family":"Yu","sequence":"first","affiliation":[{"name":"College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Youssef, M., Mah, M., and Agrawala, A. (2007, January 9\u201314). Challenges: Device-Free Passive Localization for Wireless Environments. Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking (MobiCom\u201907), Montreal, QC, Canada.","DOI":"10.1145\/1287853.1287880"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, D., Ma, J., Chen, Q., and Ni, L. (2007, January 19\u201323). An RF-Based System for Tracking Transceiver-Free Objects. Proceedings of the Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom\u201907), White Plains, NY, USA.","DOI":"10.1109\/PERCOM.2007.8"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1109\/ACCESS.2017.2784387","article-title":"Fingerprint-Based Device-Free Localization in Changing Environments using Enhanced Channel Selection and Logistic Regression","volume":"6","author":"Lei","year":"2018","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2985","DOI":"10.1109\/TCOMM.2017.2695198","article-title":"Multiple Target Counting and Localization Using Variational Bayesian EM Algorithm in Wireless Sensor Networks","volume":"65","author":"Sun","year":"2017","journal-title":"IEEE Trans. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2018.2889817","article-title":"Multitarget Localization with Inaccurate Sensor Locations via Variational EM Algorithm","volume":"3","author":"Qian","year":"2019","journal-title":"IEEE Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1109\/TCOMM.2017.2770139","article-title":"Variational Bayesian Inference-based Counting and Localization for Off-Grid Targets with Faulty Prior Information in Wireless Sensor Networks","volume":"66","author":"Guo","year":"2018","journal-title":"IEEE Trans. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/TAES.2007.4441754","article-title":"Doppler and Direction-Of-Arrival (DDOA) Radar for Multiple-Mover Sensing","volume":"43","author":"Lin","year":"2007","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_8","unstructured":"Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M., and Shafer, S. (2000, January 1). Multi-Camera Multi-Person Tracking for EasyLiving. Proceedings of the IEEE 3rd International Workshop on Visual Surveillance, Dublin, Ireland."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1145\/128756.128759","article-title":"The Active Badge Location System","volume":"10","author":"Want","year":"1992","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_10","first-page":"1162","article-title":"Adaptive Distance Estimation Based on RSSI in 802.15.4 Network","volume":"22","author":"Botta","year":"2013","journal-title":"Radioengineering"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Neburka, J., Tlamse, Z., Benes, V., Polak, L., Kaller, O., Bolecek, L., Sebesta, J., and Kratochvil, T. (2016, January 19\u201320). Study of the Performance of RSSI Based Bluetooth Smart Indoor Positioning. Proceedings of the IEEE 26th International Conference Radioelektronika (RADIOELEKTRONIKA), Kosice, Slovakia.","DOI":"10.1109\/RADIOELEK.2016.7477344"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1109\/TPDS.2012.134","article-title":"RASS: A Real-Time, Accurate, and Scalable System for Tracking Transceiver-Free Objects","volume":"24","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1109\/JSAC.2015.2430515","article-title":"Fingerprint-Based Device-Free Localization Performance in Changing Environments","volume":"33","author":"Mager","year":"2015","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9743","DOI":"10.1109\/ACCESS.2017.2649540","article-title":"Dictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithm","volume":"4","author":"Yu","year":"2016","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2543581.2543592","article-title":"From RSSI to CSI: Indoor Localization via Channel Response","volume":"46","author":"Yang","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Guo, Y., Yu, D., and Li, N. (2018). Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks. Sensors, 18.","DOI":"10.3390\/s18093110"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2007.914731","article-title":"An Introduction to Compressive Sampling","volume":"25","author":"Candes","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6665","DOI":"10.1109\/TVT.2015.2476495","article-title":"Towards Accurate Device-Free Wireless Localization with a Saddle Surface Model","volume":"65","author":"Wang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/MSP.2008.929620","article-title":"The Variational Approximation for Bayesian Inference","volume":"25","author":"Tzikas","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1109\/TVT.2016.2555986","article-title":"Device-Free Simultaneous Wireless Localization and Activity Recognition with Wavelet Feature","volume":"66","author":"Wang","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, J., Fang, D., Chen, X., Yang, Z., Xing, T., and Cai, L. (2013, January 14\u201319). LCS: Compressive Sensing Based Device-Free Localization for Multiple Targets in Sensor Networks. Proceedings of the IEEE INFOCOM 2013, Turin, Italy.","DOI":"10.1109\/INFCOM.2013.6566752"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TMC.2016.2567396","article-title":"E-HIPA: An Energy-Efficient Framework for High-Precision Multi-Target-Adaptive Device-Free Localization","volume":"16","author":"Wang","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xiao, J., Wu, K., Yi, Y., Wang, L., and Ni, L. (2013, January 8\u201311). Pilot: Passive Device-Free Indoor Localization Using Channel State Information. Proceedings of the IEEE 33rd International Conference on Distributed Computing Systems, Philadelphia, PA, USA.","DOI":"10.1109\/ICDCS.2013.49"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Abdel-Nasser, H., Samir, R., Sabek, I., and Youssef, M. (2013, January 7\u201310). Monophy: Mono-Stream-Based Device-Free WLAN Localization via Physical Layer Information. Proceedings of the IEEE Wireless Communication Network Conference (WCNC), Shanghai, China.","DOI":"10.1109\/WCNC.2013.6555311"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"10346","DOI":"10.1109\/TVT.2017.2737553","article-title":"CSI-Based Device-Free Wireless Localization and Activity Recognition Using Radio Image Features","volume":"66","author":"Gao","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2550","DOI":"10.1109\/TMC.2018.2812746","article-title":"Low Human-Effort, Device-Free Localization with Fine-Grained Subcarrier Information","volume":"17","author":"Wang","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1109\/TMC.2009.174","article-title":"Radio Tomographic Imaging with Wireless Networks","volume":"9","author":"Wilson","year":"2010","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6236","DOI":"10.1109\/TIT.2011.2162174","article-title":"Latent Variable Bayesian Models for Promoting Sparsity","volume":"57","author":"Wipf","year":"2011","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1109\/TSP.2004.831016","article-title":"Sparse Bayesian Learning for Basis Selection","volume":"52","author":"Wipf","year":"2004","journal-title":"IEEE Trans. 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