Weaving missile classification using deep learning

Vitaly Shalumov, Itzik Klein

Research output: Contribution to conferencePaperpeer-review


In target tracking estimation of maneuvering targets, the estimation of an unknown weaving target frequency is crucial for improving the miss distance. In this paper, we propose estimating the weaving frequency using deep neural networks, instead of classical estimation algorithms based on the Kalman filter framework. After designing a network, we further compare the proposed approach performance with multiple model adaptive estimation. Simulation results show that deep neural network approach outperforms multiple model adaptive approach in terms of accuracy and the amount of required measurements for the estimation, for all examined scenarios.

Original languageEnglish
Number of pages9
StatePublished - 2018
Externally publishedYes
Event58th Israel Annual Conference on Aerospace Sciences, IACAS 2018 - Tel-Aviv and Haifa, Israel
Duration: 14 Mar 201815 Mar 2018


Conference58th Israel Annual Conference on Aerospace Sciences, IACAS 2018
CityTel-Aviv and Haifa

Bibliographical note

Publisher Copyright:
© 2018 Israel Annual Conference on Aerospace Sciences. All rights reserved.

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science


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