Accurate target motion analysis from a small measurement set using RANSAC

Hyunhak Shin; Bonhwa Ku; Wooyoung Hong; Hanseok Ko

doi:10.1587/transinf.2017EDL8245

Accurate target motion analysis from a small measurement set using RANSAC

Hyunhak Shin, Bonhwa Ku, Wooyoung Hong, Hanseok Ko

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Most conventional research on target motion analysis (TMA) based on least squares (LS) has focused on performing asymptotically unbiased estimation with inaccurate measurements. However, such research may often yield inaccurate estimation results when only a small set of measurement data is used. In this paper, we propose an accurate TMA method even with a small set of bearing measurements. First, a subset of measurements is selected by a random sample consensus (RANSAC) algorithm. Then, LS is applied to the selected subset to estimate target motion. Finally, to increase accuracy, the target motion estimation is refined through a bias compensation algorithm. Simulated results verify the effectiveness of the proposed method.

Original language	English
Pages (from-to)	1711-1714
Number of pages	4
Journal	IEICE Transactions on Information and Systems
Volume	E101D
Issue number	6
DOIs	https://doi.org/10.1587/transinf.2017EDL8245
Publication status	Published - 2018 Jun

Keywords

Bearing only target motion analysis
Least squares
RANSAC

ASJC Scopus subject areas

Software
Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Artificial Intelligence

Access to Document

10.1587/transinf.2017EDL8245

Cite this

@article{f981ad8d63ba440b821eecfe03c6af88,

title = "Accurate target motion analysis from a small measurement set using RANSAC",

abstract = "Most conventional research on target motion analysis (TMA) based on least squares (LS) has focused on performing asymptotically unbiased estimation with inaccurate measurements. However, such research may often yield inaccurate estimation results when only a small set of measurement data is used. In this paper, we propose an accurate TMA method even with a small set of bearing measurements. First, a subset of measurements is selected by a random sample consensus (RANSAC) algorithm. Then, LS is applied to the selected subset to estimate target motion. Finally, to increase accuracy, the target motion estimation is refined through a bias compensation algorithm. Simulated results verify the effectiveness of the proposed method.",

keywords = "Bearing only target motion analysis, Least squares, RANSAC",

author = "Hyunhak Shin and Bonhwa Ku and Wooyoung Hong and Hanseok Ko",

note = "Funding Information: This research was funded by the Agency for Defense Development (UD160002DD).",

year = "2018",

month = jun,

doi = "10.1587/transinf.2017EDL8245",

language = "English",

volume = "E101D",

pages = "1711--1714",

journal = "IEICE Transactions on Information and Systems",

issn = "0916-8532",

publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",

number = "6",

}

TY - JOUR

T1 - Accurate target motion analysis from a small measurement set using RANSAC

AU - Shin, Hyunhak

AU - Ku, Bonhwa

AU - Hong, Wooyoung

AU - Ko, Hanseok

N1 - Funding Information: This research was funded by the Agency for Defense Development (UD160002DD).

PY - 2018/6

Y1 - 2018/6

N2 - Most conventional research on target motion analysis (TMA) based on least squares (LS) has focused on performing asymptotically unbiased estimation with inaccurate measurements. However, such research may often yield inaccurate estimation results when only a small set of measurement data is used. In this paper, we propose an accurate TMA method even with a small set of bearing measurements. First, a subset of measurements is selected by a random sample consensus (RANSAC) algorithm. Then, LS is applied to the selected subset to estimate target motion. Finally, to increase accuracy, the target motion estimation is refined through a bias compensation algorithm. Simulated results verify the effectiveness of the proposed method.

AB - Most conventional research on target motion analysis (TMA) based on least squares (LS) has focused on performing asymptotically unbiased estimation with inaccurate measurements. However, such research may often yield inaccurate estimation results when only a small set of measurement data is used. In this paper, we propose an accurate TMA method even with a small set of bearing measurements. First, a subset of measurements is selected by a random sample consensus (RANSAC) algorithm. Then, LS is applied to the selected subset to estimate target motion. Finally, to increase accuracy, the target motion estimation is refined through a bias compensation algorithm. Simulated results verify the effectiveness of the proposed method.

KW - Bearing only target motion analysis

KW - Least squares

KW - RANSAC

UR - http://www.scopus.com/inward/record.url?scp=85047986079&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047986079&partnerID=8YFLogxK

U2 - 10.1587/transinf.2017EDL8245

DO - 10.1587/transinf.2017EDL8245

M3 - Article

AN - SCOPUS:85047986079

SN - 0916-8532

VL - E101D

SP - 1711

EP - 1714

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

IS - 6

ER -

Accurate target motion analysis from a small measurement set using RANSAC

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this