Classify motion model via SVM to track underwater maneuvering target

Jiaxin Zhang; Meiqin Liu; Zhen Fan

doi:10.1109/ICSPCC46631.2019.8960733

Classify motion model via SVM to track underwater maneuvering target

Jiaxin Zhang
, Meiqin Liu
, Zhen Fan

Zhejiang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Maneuvering target tracking is one of the most important topics in marine research, and it is vital to track a target accurately. Tracking accuracy is closely related to the veracity of estimation of target's motion model. To improve the accuracy of tracking single underwater maneuvering target, this paper brings up a new method to reduce tracking error caused by switching of motion models. A support vector machine (SVM) is used to classify current motion model of the target, then state of the target is estimated by a Kalman filter (KF) with dynamic parameters. Simulation results suggest that compared with the classical interacting multiple model (IMM), algorithm proposed in this paper leads to a more satisfactory tracking root mean square error (RMSE) no matter the target maneuvers or not.

Original language	English
Title of host publication	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728117072
DOIs	https://doi.org/10.1109/ICSPCC46631.2019.8960733
State	Published - Sep 2019
Externally published	Yes
Event	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 - Dalian, Liaoning, China Duration: 20 Sep 2019 → 22 Sep 2019

Publication series

Name	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

Conference

Conference	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Country/Territory	China
City	Dalian, Liaoning
Period	20/09/19 → 22/09/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 14 Life Below Water

Keywords

Kalman filter
Maneuvering target tracking
Motion model
Support vector machine

Access to Document

10.1109/ICSPCC46631.2019.8960733

Cite this

Zhang, J., Liu, M., & Fan, Z. (2019). Classify motion model via SVM to track underwater maneuvering target. In 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 Article 8960733 (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSPCC46631.2019.8960733

Zhang, Jiaxin ; Liu, Meiqin ; Fan, Zhen. / Classify motion model via SVM to track underwater maneuvering target. 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019).

@inproceedings{69212a3cd0554e6e82f396672c63eec6,

title = "Classify motion model via SVM to track underwater maneuvering target",

abstract = "Maneuvering target tracking is one of the most important topics in marine research, and it is vital to track a target accurately. Tracking accuracy is closely related to the veracity of estimation of target's motion model. To improve the accuracy of tracking single underwater maneuvering target, this paper brings up a new method to reduce tracking error caused by switching of motion models. A support vector machine (SVM) is used to classify current motion model of the target, then state of the target is estimated by a Kalman filter (KF) with dynamic parameters. Simulation results suggest that compared with the classical interacting multiple model (IMM), algorithm proposed in this paper leads to a more satisfactory tracking root mean square error (RMSE) no matter the target maneuvers or not.",

keywords = "Kalman filter, Maneuvering target tracking, Motion model, Support vector machine",

author = "Jiaxin Zhang and Meiqin Liu and Zhen Fan",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 ; Conference date: 20-09-2019 Through 22-09-2019",

year = "2019",

month = sep,

doi = "10.1109/ICSPCC46631.2019.8960733",

language = "英语",

series = "2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019",

}

Zhang, J, Liu, M & Fan, Z 2019, Classify motion model via SVM to track underwater maneuvering target. in 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019., 8960733, 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019, Dalian, Liaoning, China, 20/09/19. https://doi.org/10.1109/ICSPCC46631.2019.8960733

Classify motion model via SVM to track underwater maneuvering target. / Zhang, Jiaxin; Liu, Meiqin; Fan, Zhen.
2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8960733 (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classify motion model via SVM to track underwater maneuvering target

AU - Zhang, Jiaxin

AU - Liu, Meiqin

AU - Fan, Zhen

PY - 2019/9

Y1 - 2019/9

N2 - Maneuvering target tracking is one of the most important topics in marine research, and it is vital to track a target accurately. Tracking accuracy is closely related to the veracity of estimation of target's motion model. To improve the accuracy of tracking single underwater maneuvering target, this paper brings up a new method to reduce tracking error caused by switching of motion models. A support vector machine (SVM) is used to classify current motion model of the target, then state of the target is estimated by a Kalman filter (KF) with dynamic parameters. Simulation results suggest that compared with the classical interacting multiple model (IMM), algorithm proposed in this paper leads to a more satisfactory tracking root mean square error (RMSE) no matter the target maneuvers or not.

AB - Maneuvering target tracking is one of the most important topics in marine research, and it is vital to track a target accurately. Tracking accuracy is closely related to the veracity of estimation of target's motion model. To improve the accuracy of tracking single underwater maneuvering target, this paper brings up a new method to reduce tracking error caused by switching of motion models. A support vector machine (SVM) is used to classify current motion model of the target, then state of the target is estimated by a Kalman filter (KF) with dynamic parameters. Simulation results suggest that compared with the classical interacting multiple model (IMM), algorithm proposed in this paper leads to a more satisfactory tracking root mean square error (RMSE) no matter the target maneuvers or not.

KW - Kalman filter

KW - Maneuvering target tracking

KW - Motion model

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/85078922154

U2 - 10.1109/ICSPCC46631.2019.8960733

DO - 10.1109/ICSPCC46631.2019.8960733

M3 - 会议稿件

AN - SCOPUS:85078922154

T3 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

BT - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

Y2 - 20 September 2019 through 22 September 2019

ER -

Zhang J, Liu M, Fan Z. Classify motion model via SVM to track underwater maneuvering target. In 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8960733. (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019). doi: 10.1109/ICSPCC46631.2019.8960733

Classify motion model via SVM to track underwater maneuvering target

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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