Fast Parameterless Ballistic Launch Point Estimation based on k-NN Search

Soojin Kim; Hyunjoong Kim; Sungzoon Cho

doi:10.14429/dsj.64.2952

Soojin Kim Seoul National University, Seoul
Hyunjoong Kim Seoul National University, Seoul
Sungzoon Cho Seoul National University, Seoul

DOI: https://doi.org/10.14429/dsj.64.2952

Keywords: k-NN search, iDistance, trajectory similarity search, launch point estimation

Abstract

This paper discusses the problem of estimating a ballistic trajectory and the launch point by using a trajectory similarity search in a database. The major difficulty of this problem is that estimation accuracy is guaranteed only when an identical trajectory exists in the trajectory database (TD). Hence, the TD must comprise an impractically great number of trajectories from various launch points. Authors proposed a simplified trajectory database with a single launch point and a trajectory similarity search algorithm that decomposes trajectory similarity into velocity and position components. These similarities are applied k-NN estimation. Furthermore, they used the iDistance technique to partition the data space of the high-dimensional database for an efficient k-NN search. Authors proved the effectiveness of the proposed algorithm by experiment.

Defence Science Journal, Vol. 64, No. 1, January 2014, DOI:10.14429/dsj.64.2952

Author Biographies

Soojin Kim, Seoul National University, Seoul

Dr Soojin Kim received her Masters degree (Inderstrial Engineering) from KAIST, in 2002 and PhD (Datamining) from Seoul National University, in 2013. Presently, she is working as Army Major in Defense Agency for Technology and Quality.

Hyunjoong Kim, Seoul National University, Seoul

Mr Hyunjoong Kim recieved his Masters degree of Industrial Engineering from Seoul National University in 2013. Presently, he is doctoral student of Industrial Engineering in Seoul National University.

Sungzoon Cho, Seoul National University, Seoul

Prof. Sungzoon Cho received his doctoral degree in computer science at the University of Maryland in College Park with research on machine learning. Presently, he is deputy director of Big Data Center at Seoul National University and an editor of International Journal of Operations Research and Information Systems (IJORIS) and International Journal of Cognitive Biometrics (IJCB).