{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:22:37Z","timestamp":1767183757277,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Keio Leading-Edge Laboratory of Science and Technology","award":["KEIO-KLL-000030"],"award-info":[{"award-number":["KEIO-KLL-000030"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.<\/jats:p>","DOI":"10.3390\/s22239401","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T03:28:04Z","timestamp":1669951684000},"page":"9401","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar"],"prefix":"10.3390","volume":"22","author":[{"given":"Koji","family":"Endo","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kohei","family":"Yamamoto","sequence":"additional","affiliation":[{"name":"Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-1426","authenticated-orcid":false,"given":"Tomoaki","family":"Ohtsuki","sequence":"additional","affiliation":[{"name":"Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Knudde, N., Vandersmissen, B., Parashar, K., Couckuyt, I., Jalalvand, A., Bourdoux, A., and Dhaene, T. (2017, January 11\u201313). Indoor tracking of multiple persons with a 77 GHz MIMO FMCW radar. Proceedings of the 2017 European Radar Conference (EURAD), Nuremberg, Germany.","DOI":"10.23919\/EURAD.2017.8249147"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3941","DOI":"10.1109\/TGRS.2018.2816812","article-title":"Indoor Person Identification Using a Low-Power FMCW Radar","volume":"56","author":"Vandersmissen","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13522","DOI":"10.1109\/JSEN.2021.3068388","article-title":"Human Activity Classification Based on Point Clouds Measured by Millimeter Wave MIMO Radar with Deep Recurrent Neural Networks","volume":"21","author":"Kim","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aziz, F., Metwally, O., Weller, P., Schneider, U., and Huber, M.F. (2022, January 21\u201325). A MIMO Radar-Based Metric Learning Approach for Activity Recognition. Proceedings of the 2022 IEEE Radar Conference (RadarConf22), New York, NY, USA.","DOI":"10.1109\/RadarConf2248738.2022.9764202"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lei, W., Jiang, X., Tan, Q., Xu, L., Zhao, Y., Xu, T., Li, Y., Gu, Q., Liu, G., and Zhao, Y. (2019, January 11\u201313). A TD-CF preprocessing method of FMCW radar for Dynamic Hand Gesture Recognition. Proceedings of the 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China.","DOI":"10.1109\/ICSIDP47821.2019.9173196"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yamamoto, K., Endo, K., and Ohtsuki, T. (2021, January 7\u201311). Remote Sensing of Heartbeat based on Space Diversity Using MIMO FMCW Radar. Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain.","DOI":"10.1109\/GLOBECOM46510.2021.9685033"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106895","DOI":"10.1109\/ACCESS.2021.3099821","article-title":"Noncontact Respiratory Measurement for Multiple People at Arbitrary Locations Using Array Radar and Respiratory-Space Clustering","volume":"9","author":"Koda","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alizadeh, M., Shaker, G., and Safavi-Naeini, S. (2020, January 26\u201328). Remote Health Monitoring System for Bedbound Patients. Proceedings of the 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), Cincinnati, OH, USA.","DOI":"10.1109\/BIBE50027.2020.00136"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, W., Wang, Y., Zhou, M., and Nie, W. (2020, January 8\u201311). A Novel Vital Sign Sensing Algorithm for Multiple People Detection Based on FMCW Radar. Proceedings of the 2020 IEEE Asia-Pacific Microwave Conference (APMC), Hong Kong, China.","DOI":"10.1109\/APMC47863.2020.9331552"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106017","DOI":"10.1109\/ACCESS.2022.3211527","article-title":"Vital Sign Detection via Angular and Range Measurements with mmWave MIMO Radars: Algorithms and Trials","volume":"10","author":"Upadhyay","year":"2022","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5348","DOI":"10.1109\/TMTT.2019.2939523","article-title":"Phase-Based Human Target 2-D Identification With a Mobile FMCW Radar Platform","volume":"67","author":"Yan","year":"2019","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1109\/LMWC.2015.2463214","article-title":"Noncontact multiple heartbeats detection and subject localization using UWB impulse doppler radar","volume":"25","author":"Ren","year":"2015","journal-title":"IEEE Microw. Wirel. Compon. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, S.G., Ko, I.C., and Jung, S.H. (2020, January 4\u20136). High Resolution CMOS IR-UWB Radar for Non-Contact Human Vital Signs Detection. Proceedings of the 2020 IEEE Radio Frequency Integrated Circuits Symposium (RFIC), Los Angeles, CA, USA.","DOI":"10.1109\/RFIC49505.2020.9218284"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"22179","DOI":"10.1109\/JSEN.2022.3210256","article-title":"Automatic Contact-less Monitoring of Breathing Rate and Heart Rate utilizing the Fusion of mmWave Radar and Camera Steering System","volume":"22","author":"Gupta","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kocur, D., Porteleky, T., and \u0160vecov\u00e1, M. (2019, January 27\u201330). UWB Radar Testbed System for Localization of Multiple Static Persons. Proceedings of the 2019 IEEE SENSORS, Montreal, QC, Canada.","DOI":"10.1109\/SENSORS43011.2019.8956782"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, B.-H., and Yook, J.-G. (2018, January 5\u20138). Path Loss Compensation Method for Multiple Target Vital Sign Detection with 24-GHz FMCW Radar. Proceedings of the 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP), Auckland, New Zealand.","DOI":"10.1109\/APCAP.2018.8538182"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1109\/TBCAS.2021.3123830","article-title":"Accurate Measurement of Human Vital Signs With Linear FMCW Radars Under Proximity Stationary Clutters","volume":"15","author":"Liu","year":"2021","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3218574","article-title":"Detection of Human Breathing in Non-Line-of-Sight Region by Using mmWave FMCW Radar","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","unstructured":"Skolnik, M.I. (2008). Introduction to RADAR Systems, McGraw-Hill. [3rd ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1109\/LMWC.2021.3057867","article-title":"Detection and Localization of Multiple Humans Based on Curve Length of I\/Q Signal Trajectory Using MIMO FMCW Radar","volume":"31","author":"Kawon","year":"2021","journal-title":"IEEE Microw. Wirel. Components Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kang, S., Jang, M., and Lee, S. (2022). Autoencoder-Based Target Detection in Automotive MIMO FMCW Radar System. Sensors, 22.","DOI":"10.3390\/s22155552"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1109\/TWC.2019.2962474","article-title":"Massive MIMO channel estimation with an untrained deep neural network","volume":"19","author":"Balevi","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_23","unstructured":"Endo, K., Yamamoto, K., and Ohtsuki, T. (2021, January 1\u20133). A Study on Denoising Method Using Deep Image Prior for Radar Signal Processing. Proceedings of the 2021 International Conference on Emerging Technologies for Communications (ICETC2021), Online."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1109\/LSP.2019.2939049","article-title":"Deep Learning Denoising Based Line Spectral Estimation","volume":"26","author":"Jiang","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_25","unstructured":"(2022, September 28). Train and Apply Denoising Neural Networks\u2014MATLAB & Simulink. Available online: https:\/\/www.mathworks.com\/help\/images\/train-and-apply-denoising-neural-networks.html."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1007\/s11263-020-01303-4","article-title":"Deep Image Prior","volume":"128","author":"Ulyanov","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lempitsky, V., Vedaldi, A., and Ulyanov, D. (2018, January 18\u201323). Deep image prior. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00984"},{"key":"ref_28","unstructured":"(2022, September 28). GitHub\u2014DmitryUlyanov\/Deep-Image-Prior. Available online: https:\/\/github.com\/DmitryUlyanov\/deep-image-prior."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9401\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:32:36Z","timestamp":1760146356000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9401"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,2]]},"references-count":28,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239401"],"URL":"https:\/\/doi.org\/10.3390\/s22239401","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,12,2]]}}}