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Open AccessArticle

Practical Methods for Vehicle Speed Estimation Using a Microprocessor-Embedded System with AMR Sensors

1
Department of Electronics Engineering, Kaunas University of Technology, Studentu Street 50–418, LT-51368 Kaunas, Lithuania
2
Faculty of Electrical Engineering, Bialystok University of Technology; Wiejska Street 45D, PL-15351 Bialystok, Poland
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2225; https://doi.org/10.3390/s18072225
Received: 30 May 2018 / Revised: 30 June 2018 / Accepted: 9 July 2018 / Published: 10 July 2018
(This article belongs to the Special Issue Sensor Networks for Smart Roads)
The proper operation of computing resources in a microprocessor-embedded system plays a key role in reducing computing time. Processing the variable amount of collected data in real-time improves the performance of a microprocessor-embedded system. In this regard, a vehicle’s speed measurement system is no exception. The computing time for evaluating any speed value is expected to be reduced as much as possible. Four computational methods, including cross-correlation, are discussed. An exemplary pair of recorded signals presenting the change in magnetic field magnitude is analyzed. The sample delay values are compared. The results of the evaluated speed and the execution time of the program code are presented for each method based on a dataset of 200 randomly driven vehicles. The results of the performed tests confirm that the cross-correlation-based methods are not always reliable in situations when the sample size is small, i.e., it is a segment of the impulse response caused by a driving vehicle. View Full-Text
Keywords: magnetic field measurement; magnetic sensors; speed estimation; error analysis; computational complexity magnetic field measurement; magnetic sensors; speed estimation; error analysis; computational complexity
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MDPI and ACS Style

Markevicius, V.; Navikas, D.; Idzkowski, A.; Andriukaitis, D.; Valinevicius, A.; Zilys, M. Practical Methods for Vehicle Speed Estimation Using a Microprocessor-Embedded System with AMR Sensors. Sensors 2018, 18, 2225. https://doi.org/10.3390/s18072225

AMA Style

Markevicius V, Navikas D, Idzkowski A, Andriukaitis D, Valinevicius A, Zilys M. Practical Methods for Vehicle Speed Estimation Using a Microprocessor-Embedded System with AMR Sensors. Sensors. 2018; 18(7):2225. https://doi.org/10.3390/s18072225

Chicago/Turabian Style

Markevicius, Vytautas; Navikas, Dangirutis; Idzkowski, Adam; Andriukaitis, Darius; Valinevicius, Algimantas; Zilys, Mindaugas. 2018. "Practical Methods for Vehicle Speed Estimation Using a Microprocessor-Embedded System with AMR Sensors" Sensors 18, no. 7: 2225. https://doi.org/10.3390/s18072225

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