Findings of Radar Cross Section Using AI and ML

Authors

  • R. Shandilya Department of Electronics and Communication Engineering, Netaji Subhash University of Technology (East Campus), New Delhi, India.
  • Dr. Manisha Khulbe Netaji Subhash University of Technology Delhi (East Campus), New Delhi, India.
  • A. Jain Department of Electronics and Communication Engineering, Netaji Subhash University of Technology (East Campus), New Delhi, India.
  • S. Kaushik Department of Electronics and Communication Engineering, Netaji Subhash University of Technology (East Campus), New Delhi, India.
  • R. Yadav Department of Electronics and Communication Engineering, Netaji Subhash University of Technology (East Campus), New Delhi, India.

DOI:

https://doi.org/10.9734/bpi/mono/978-93-49238-41-1/CH4

Keywords:

Radar cross-section, simulation, modelling, CST studio suite, classification

Abstract

This study aimed to discuss the monostatic and bistatic radar cross-section of an object with the help of simulation tools and to relate the findings with the radar cross-section definition using MATLAB. A PEC cylinder and sphere were used as an object for the simulation of the Radar Cross Section computed in CST Studio Suite software. The incident wave selected is normal and plane with TE polarization. The setup here discusses the different findings of the Radar Cross Section at different parameters like azimuth, elevation, and frequency and compares it with the model based on the theoretical formula. After simulation, results are compared with the theoretical formula using the modelling in MATLAB software.

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Published

2025-02-14

How to Cite

R. Shandilya, Dr. Manisha Khulbe, A. Jain, S. Kaushik, & R. Yadav. (2025). Findings of Radar Cross Section Using AI and ML. Design and Simulation of Classical and Quantum Antennas in Gigahertz and Terahertz: Applications in Radar Using Deep Neural Techniques, 75–89. https://doi.org/10.9734/bpi/mono/978-93-49238-41-1/CH4

Issue

Design and Simulation of Classical and Quantum Antennas in Gigahertz and Terahertz: Applications in Radar Using Deep Neural Techniques

Section

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