{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:52:40Z","timestamp":1740149560529,"version":"3.37.3"},"reference-count":30,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T00:00:00Z","timestamp":1666915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Council of Lithuania (LMTLT)","award":["S-MIP-21-34"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Magnetic field sensors installed in the road infrastructure can be used for autonomous traffic flow parametrization. Although the main goal of such a measuring system is the recognition of the class of vehicle and classification, velocity is the essential parameter for further calculation and it must be estimated with high reliability. In-field test campaigns, during actual traffic conditions, showed that commonly accepted velocity estimation methods occasionally produce highly erroneous results. For anomaly detection, we propose a criterion and two different correction algorithms. Non-linear signal rescaling and time-based segmentation algorithms are presented and compared for faulty result mitigation. The first one consists of suppressing the highly distorted signal peaks and looking for the best match with cross-correlation. The second approach relies on signals segmentation according to the feature points and multiple cross-correlation comparisons. The proposed two algorithms are evaluated with a dataset of over 300 magnetic signatures of a vehicle from unconstraint traffic conditions. Results show that the proposed criteria highlight all greatly faulty results and that the correction algorithms reduce the maximum error by twofold, but due to the increased mean error, mitigation technics shall be used explicitly with distorted signals.<\/jats:p>","DOI":"10.3390\/s22218269","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T14:47:57Z","timestamp":1667141277000},"page":"8269","source":"Crossref","is-referenced-by-count":4,"title":["Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors"],"prefix":"10.3390","volume":"22","author":[{"given":"Donatas","family":"Miklusis","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-438, 51368 Kaunas, Lithuania"}]},{"given":"Vytautas","family":"Markevicius","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-438, 51368 Kaunas, Lithuania"}]},{"given":"Dangirutis","family":"Navikas","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-438, 51368 Kaunas, Lithuania"}]},{"given":"Mantas","family":"Ambraziunas","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-438, 51368 Kaunas, Lithuania"}]},{"given":"Mindaugas","family":"Cepenas","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-438, 51368 Kaunas, Lithuania"}]},{"given":"Algimantas","family":"Valinevicius","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-438, 51368 Kaunas, Lithuania"}]},{"given":"Mindaugas","family":"Zilys","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-438, 51368 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6721-3241","authenticated-orcid":false,"given":"Krzysztof","family":"Okarma","sequence":"additional","affiliation":[{"name":"Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, 70313 Szczecin, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8391-1947","authenticated-orcid":false,"given":"Inigo","family":"Cuinas","sequence":"additional","affiliation":[{"name":"Communications-atlanTTic Research Center, Department of Signal Theory, University of Vigo, 36310 Vigo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9862-8917","authenticated-orcid":false,"given":"Darius","family":"Andriukaitis","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-438, 51368 Kaunas, Lithuania"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1007\/s11277-019-06144-0","article-title":"Internet of Vehicles based approach for road safety applications using sensor technologies","volume":"105","author":"Soni","year":"2019","journal-title":"Wirel. 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