Accurate target motion analysis from a small measurement set using RANSAC

Hyunhak Shin, Bonhwa Ku, Wooyoung Hong, Hanseok Ko

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Pages (from-to)1711-1714
Number of pages4
JournalIEICE Transactions on Information and Systems
Issue number6
Publication statusPublished - 2018 Jun


  • Bearing only target motion analysis
  • Least squares

ASJC Scopus subject areas

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


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