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.