Abstract
The track-to-track fusion (T2TF) algorithm is currently an attractive fusion methodology in the industry because the algorithm can reflect the reliability of sensor tracks. However, the T2TF algorithm cannot be applied when the probability information of the sensor is unknown. The aim of this study is to exploit the T2TF algorithm even in the absence of the probability information of the sensor. The covariance is estimated using the recursive equations of the Kalman filter. In addition, a novel track-association approach using the total similarity is developed to improve association performance. The total similarity complements the defects of the track disposition and the estimated track history. Finally, by fusing the associated tracks using the estimated covariance, the T2TF algorithm is successfully applied to sensors with an unknown covariance. The fusion results are then evaluated using the correct association rate and the optimal subpattern assignment metric. The simulation results obtained show the superiority of the proposed algorithm under three scenarios.
| Original language | English |
|---|---|
| Pages (from-to) | 1386-1395 |
| Number of pages | 10 |
| Journal | IET Intelligent Transport Systems |
| Volume | 12 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - 2018 Dec 1 |
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ASJC Scopus subject areas
- Transportation
- Environmental Science(all)
- Mechanical Engineering
- Law
Cite this
Sensor fusion for vehicle tracking based on the estimated probability. / Lee, Chang Joo; Kim, Kyeong Eun; Lim, Myo Taeg.
In: IET Intelligent Transport Systems, Vol. 12, No. 10, 01.12.2018, p. 1386-1395.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Sensor fusion for vehicle tracking based on the estimated probability
AU - Lee, Chang Joo
AU - Kim, Kyeong Eun
AU - Lim, Myo Taeg
PY - 2018/12/1
Y1 - 2018/12/1
N2 - The track-to-track fusion (T2TF) algorithm is currently an attractive fusion methodology in the industry because the algorithm can reflect the reliability of sensor tracks. However, the T2TF algorithm cannot be applied when the probability information of the sensor is unknown. The aim of this study is to exploit the T2TF algorithm even in the absence of the probability information of the sensor. The covariance is estimated using the recursive equations of the Kalman filter. In addition, a novel track-association approach using the total similarity is developed to improve association performance. The total similarity complements the defects of the track disposition and the estimated track history. Finally, by fusing the associated tracks using the estimated covariance, the T2TF algorithm is successfully applied to sensors with an unknown covariance. The fusion results are then evaluated using the correct association rate and the optimal subpattern assignment metric. The simulation results obtained show the superiority of the proposed algorithm under three scenarios.
AB - The track-to-track fusion (T2TF) algorithm is currently an attractive fusion methodology in the industry because the algorithm can reflect the reliability of sensor tracks. However, the T2TF algorithm cannot be applied when the probability information of the sensor is unknown. The aim of this study is to exploit the T2TF algorithm even in the absence of the probability information of the sensor. The covariance is estimated using the recursive equations of the Kalman filter. In addition, a novel track-association approach using the total similarity is developed to improve association performance. The total similarity complements the defects of the track disposition and the estimated track history. Finally, by fusing the associated tracks using the estimated covariance, the T2TF algorithm is successfully applied to sensors with an unknown covariance. The fusion results are then evaluated using the correct association rate and the optimal subpattern assignment metric. The simulation results obtained show the superiority of the proposed algorithm under three scenarios.
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UR - http://www.scopus.com/inward/citedby.url?scp=85057065163&partnerID=8YFLogxK
U2 - 10.1049/iet-its.2018.5024
DO - 10.1049/iet-its.2018.5024
M3 - Article
AN - SCOPUS:85057065163
VL - 12
SP - 1386
EP - 1395
JO - IET Intelligent Transport Systems
JF - IET Intelligent Transport Systems
SN - 1751-956X
IS - 10
ER -