RESTORATION OF DEFECTS AND BLIND ZONE ON IMAGES OF UNDERLYING SURFACE FOR ONBOARD RADAR SYSTEMS OF MAPPING BASED ON DOPPLER BEAM SHARPENING
Abstract
The problem of forming a radar image (RI) of the earth's surface in real time remains one of
the most urgent in solving radio imaging problems, despite the appearance of a large number of
publications in this area, reflecting a whole range of new methods and algorithms for processing
trajectory signals in order to improve the quality of images. The main goal in the formation of
radar images is to achieve the maximum resolution and image quality under real constraints associated
with the drift of the parameters of the received trajectory signal (synthesis time), measurement
inaccuracy and variability of flight characteristics (speed, acceleration, flight trajectory),
exposure to a wide range of noise and interference, both external and internal, against the background
of a low-power received signal from remote radio reflectors (energy resources). The article
investigates an algorithm for constructing and restoring images of the underlying surface and
develops its software implementation. The effectiveness of the new approach is shown using several examples for various areas of the underlying surface with a blind spot. The subject of the research
is methods and algorithms for constructing a terrain map and reconstructing lost image
areas. The research object is a set of test images. The result of the research is the development of a
method for image restoration in order to restore the lost area. The novelty of the work is an algorithm
that improves the quality of image restoration based on a neural network. The results obtained
make it possible to restore the areas. Evaluation of the efficiency of the image restoration
method was carried out using a statistical criterion - the root mean square error of the processing
result from the true image. As a result of solving the tasks, we can draw conclusions: A method
was developed for constructing and restoring images of the underlying surface based on the
search for similar blocks with their subsequent combining by a neural network. Analysis of the
results of the study showed that the proposed method improves the quality of image reconstruction.
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