Target motion analysis with evolutionary search by fusion of two moving acoustic sensors

Hyunhak Shin; Wooyoung Hong; Ria Kim; Hanseok Ko

doi:10.1109/MFI.2017.8170436

Target motion analysis with evolutionary search by fusion of two moving acoustic sensors

Hyunhak Shin, Wooyoung Hong, Ria Kim, Hanseok Ko

School of Electrical Engineering

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

Abstract

This paper focuses on target motion analysis by fusion of information from two moving acoustic sensors. These two sensors may obtain small measurements in order to make quick analysis of moving targets. In this situation, conventional approaches often fail to find accurate motion of targets. In this paper, a fusion algorithm for target motion analysis designed to handle this situation is proposed. First, optimization based PSO is applied in order to find accurate initial motion of targets. Second, the target trajectory is estimated via a sequential fusion algorithm based on UKF. According to the various simulated results, the effectiveness of the proposed method is then verified.

Original language	English
Title of host publication	MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	247-252
Number of pages	6
Volume	2017-November
ISBN (Electronic)	9781509060641
DOIs	https://doi.org/10.1109/MFI.2017.8170436
Publication status	Published - 2017 Dec 7
Event	13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 - Daegu, Korea, Republic of Duration: 2017 Nov 16 → 2017 Nov 18

Other

Other	13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017
Country/Territory	Korea, Republic of
City	Daegu
Period	17/11/16 → 17/11/18

ASJC Scopus subject areas

Control and Systems Engineering
Software
Computer Science Applications

Access to Document

10.1109/MFI.2017.8170436

Cite this

Shin, H., Hong, W., Kim, R., & Ko, H. (2017). Target motion analysis with evolutionary search by fusion of two moving acoustic sensors. In MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (Vol. 2017-November, pp. 247-252). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MFI.2017.8170436

Target motion analysis with evolutionary search by fusion of two moving acoustic sensors. / Shin, Hyunhak; Hong, Wooyoung; Kim, Ria et al.
MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Vol. 2017-November Institute of Electrical and Electronics Engineers Inc., 2017. p. 247-252.

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

Shin, H, Hong, W, Kim, R & Ko, H 2017, Target motion analysis with evolutionary search by fusion of two moving acoustic sensors. in MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. vol. 2017-November, Institute of Electrical and Electronics Engineers Inc., pp. 247-252, 13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017, Daegu, Korea, Republic of, 17/11/16. https://doi.org/10.1109/MFI.2017.8170436

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abstract = "This paper focuses on target motion analysis by fusion of information from two moving acoustic sensors. These two sensors may obtain small measurements in order to make quick analysis of moving targets. In this situation, conventional approaches often fail to find accurate motion of targets. In this paper, a fusion algorithm for target motion analysis designed to handle this situation is proposed. First, optimization based PSO is applied in order to find accurate initial motion of targets. Second, the target trajectory is estimated via a sequential fusion algorithm based on UKF. According to the various simulated results, the effectiveness of the proposed method is then verified.",

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Target motion analysis with evolutionary search by fusion of two moving acoustic sensors

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