Event Title

Design of Automatic Target Recognition system based on multistatic passive RADAR

Session

Computer Science and Communication Engineering

Description

Nowadays, a great number of researchers are concerned about aerial targets since they are involved in many aspects of everyday life, such as military issues, including aircrafts and missiles, or Unmanned Aerial Vehicles (UAVs) flying over city centers or airport areas. The design of an efficient Automatic Target Recognition (ATR) system has been an attractive problem and for this reason many researchers rely on experiments to extract the necessary data for the ATR system. In this paper, a new method for extracting the radar cross-section (RCS) data has been proposed, which uses the Boundary Element Method (BEM) to efficiently compute the RCS values of different objects at any point of the coordinate system in a short period of time, without need of any experiment. Multiple RCS values in the presence of noise are used for the training and the evaluation of two ATR systems, based on the nearest neighbor classification rule or a multilayer Neural Network.

Keywords:

Automatic target recognition (ATR), bistatic radar, radar cross- section (RCS), classification, machine learning, neural networks

Session Chair

Bertan Karahoda

Session Co-Chair

Krenare Pireva

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-437-69-1

Location

Pristina, Kosovo

Start Date

27-10-2018 10:45 AM

End Date

27-10-2018 12:15 PM

DOI

10.33107/ubt-ic.2018.89

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Oct 27th, 10:45 AM Oct 27th, 12:15 PM

Design of Automatic Target Recognition system based on multistatic passive RADAR

Pristina, Kosovo

Nowadays, a great number of researchers are concerned about aerial targets since they are involved in many aspects of everyday life, such as military issues, including aircrafts and missiles, or Unmanned Aerial Vehicles (UAVs) flying over city centers or airport areas. The design of an efficient Automatic Target Recognition (ATR) system has been an attractive problem and for this reason many researchers rely on experiments to extract the necessary data for the ATR system. In this paper, a new method for extracting the radar cross-section (RCS) data has been proposed, which uses the Boundary Element Method (BEM) to efficiently compute the RCS values of different objects at any point of the coordinate system in a short period of time, without need of any experiment. Multiple RCS values in the presence of noise are used for the training and the evaluation of two ATR systems, based on the nearest neighbor classification rule or a multilayer Neural Network.