Name
Title | ||
|---|---|---|
Practical Methods for Vehicle Speed Estimation Using a Microprocessor-Embedded System with AMR Sensors. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18072225 | SENSORS |
Keywords | Field | DocType |
magnetic field measurement,magnetic sensors,speed estimation,error analysis,computational complexity | Microprocessor,Engineering,Embedded system | Journal |
Volume | Issue | Citations |
18 | 7.0 | 1 |
PageRank | References | Authors |
0.43 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vytautas Markevicius | 1 | 12 | 4.05 |
| Dangirutis Navikas | 2 | 13 | 4.74 |
| Adam Idzkowski | 3 | 4 | 1.57 |
| Darius Andriukaitis | 4 | 13 | 6.09 |
| Algimantas Valinevicius | 5 | 12 | 5.40 |
| Mindaugas Zilys | 6 | 13 | 4.22 |