Current aspects of synthetic aperture radar


Аuthors

Stepanov G. Y.*, Nenashev V. A.**

Saint Petersburg State University of Aerospace Instrumentation, 67, Bolshaya Morskaya str., Saint Petersburg, 190000, Russia

*e-mail: georgy.nettov@yandex.ru
**e-mail: nenashev.va@yandex.ru

Abstract

This article summarizes and analyzes current aspects of airborne synthetic aperture radar. The objective of the work is to thoroughly examine the current state of this field and its development prospects. To gather the necessary information, a search of relevant sources in scientific databases over the past several years was conducted, followed by a comparison and evaluation of the search results. Particular attention is paid to innovations in synthetic aperture radar. It is shown that compact synthetic aperture radars based on small aircraft increase the capabilities of Earth remote sensing due to various application variations and cost-effective solutions. Digital beamforming improves adaptability to various situations in beam pattern control, multipath mode, and increasing the dynamic range. Artificial intelligence and machine learning methods simplify and improve the processing of synthetic aperture radar data. Emphasis is placed on problems associated with platform motion compensation and labor-intensive onboard and ground processing of large volumes of data. The role of simulation modeling as a necessary tool for testing algorithms, calibrating systems, and generating data for training artificial intelligence models is examined separately. The interdependence of these areas is revealed, demonstrating that each enhances the capabilities of the other, leading to the development of technology and its increased effectiveness in various tasks. The article emphasizes the need to improve synthetic aperture radar systems, as well as the development of devices for onboard data processing using artificial intelligence and machine learning.

Keywords:

airborne radar; synthetic aperture; Earth remote sensing; small aircraft; digital beamforming; artificial intelligence; machine learning; data processing; simulation

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