Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors
To avoid the inherent defects of current airport surface surveillance systems, a distributed non-cooperative surface surveillance scheme based on geomagnetic sensor technology is proposed in this article. Furthermore, a surface target tracking algorithm based on improved interacting multiple model (...
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2020-02-01
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Series: | International Journal of Distributed Sensor Networks |
Online Access: | https://doi.org/10.1177/1550147720904563 |
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doaj-56367f73e2304085b359345fa8710f5d2020-11-25T03:54:36ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772020-02-011610.1177/1550147720904563Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensorsXinmin Tang0Wenjie Zhao1Shangfeng Gao2College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCommercial Aircraft Corporation of China, Ltd., Shanghai, ChinaTo avoid the inherent defects of current airport surface surveillance systems, a distributed non-cooperative surface surveillance scheme based on geomagnetic sensor technology is proposed in this article. Furthermore, a surface target tracking algorithm based on improved interacting multiple model (WIMM) is presented for use when the target is perceptible. In this algorithm, the weighted sum of the mean values of the residual errors, which is used to reconstruct the model probabilistic likelihood function, and the Markov model transition probability are updated using posterior information. When a target is imperceptible, its trajectory can be predicted by the target identified motion model and the adaptive model transition probability. Simulation results show that the WIMM algorithm can be used efficiently together with an observed small sample of velocity information for target tracking and trajectory prediction. Compared with the interacting multiple model and residual-mean interacting multiple model algorithms, the frequency of model switching and the rate of model identification were increased during the imperceptible period, and target prediction error was greatly reduced.https://doi.org/10.1177/1550147720904563 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xinmin Tang Wenjie Zhao Shangfeng Gao |
spellingShingle |
Xinmin Tang Wenjie Zhao Shangfeng Gao Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors International Journal of Distributed Sensor Networks |
author_facet |
Xinmin Tang Wenjie Zhao Shangfeng Gao |
author_sort |
Xinmin Tang |
title |
Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors |
title_short |
Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors |
title_full |
Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors |
title_fullStr |
Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors |
title_full_unstemmed |
Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors |
title_sort |
improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors |
publisher |
SAGE Publishing |
series |
International Journal of Distributed Sensor Networks |
issn |
1550-1477 |
publishDate |
2020-02-01 |
description |
To avoid the inherent defects of current airport surface surveillance systems, a distributed non-cooperative surface surveillance scheme based on geomagnetic sensor technology is proposed in this article. Furthermore, a surface target tracking algorithm based on improved interacting multiple model (WIMM) is presented for use when the target is perceptible. In this algorithm, the weighted sum of the mean values of the residual errors, which is used to reconstruct the model probabilistic likelihood function, and the Markov model transition probability are updated using posterior information. When a target is imperceptible, its trajectory can be predicted by the target identified motion model and the adaptive model transition probability. Simulation results show that the WIMM algorithm can be used efficiently together with an observed small sample of velocity information for target tracking and trajectory prediction. Compared with the interacting multiple model and residual-mean interacting multiple model algorithms, the frequency of model switching and the rate of model identification were increased during the imperceptible period, and target prediction error was greatly reduced. |
url |
https://doi.org/10.1177/1550147720904563 |
work_keys_str_mv |
AT xinmintang improvedinteractingmultiplemodelalgorithmairportsurfacetargettrackingbasedongeomagneticsensors AT wenjiezhao improvedinteractingmultiplemodelalgorithmairportsurfacetargettrackingbasedongeomagneticsensors AT shangfenggao improvedinteractingmultiplemodelalgorithmairportsurfacetargettrackingbasedongeomagneticsensors |
_version_ |
1724472809614213120 |