Radar target recognition by machine learning of k-nearest neighbors regression on angular diversity RCS

Research output: Contribution to journalArticle

Abstract

In this paper, the radar target recognition is given by machine learning of K-NN (K-nearest neighbors) regression on angular diversity RCS (radar cross section). The bistatic RCS of a target at a fixed elevation angle and different azimuth angles are collected to constitute an angular diversity RCS vector. Such angular diversity RCS vectors are chosen as features to identify the target. Different RCS vectors are collected and processed by the K-NN regression. The machine learning belongs to the scope of artificial intelligence, which has attracted the attention of researchers all over the world. In this study, the K-NN rule is extended to achieve regression and is then applied to radar target recognition. With the use of K-NN regression, the radar target recognition is very simple, efficient, and accurate. Numerical simulation results show that our target recognition scheme is not only accurate, but also has good ability to tolerate random fluctuations.

Original languageEnglish
Pages (from-to)75-81
Number of pages7
JournalApplied Computational Electromagnetics Society Journal
Volume34
Issue number1
Publication statusPublished - 2019 Jan 1

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Radar target recognition
radar targets
radar cross sections
machine learning
Radar cross section
target recognition
Learning systems
regression analysis
multistatic radar
elevation angle
artificial intelligence
azimuth
Artificial intelligence
Computer simulation

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Electrical and Electronic Engineering

Cite this

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abstract = "In this paper, the radar target recognition is given by machine learning of K-NN (K-nearest neighbors) regression on angular diversity RCS (radar cross section). The bistatic RCS of a target at a fixed elevation angle and different azimuth angles are collected to constitute an angular diversity RCS vector. Such angular diversity RCS vectors are chosen as features to identify the target. Different RCS vectors are collected and processed by the K-NN regression. The machine learning belongs to the scope of artificial intelligence, which has attracted the attention of researchers all over the world. In this study, the K-NN rule is extended to achieve regression and is then applied to radar target recognition. With the use of K-NN regression, the radar target recognition is very simple, efficient, and accurate. Numerical simulation results show that our target recognition scheme is not only accurate, but also has good ability to tolerate random fluctuations.",
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