APPLICATION OF DIFFUSION MODELS FOR RADAR SIGNAL SYNTHESIS AND IMPROVEMENT OF OBJECT DETECTION QUALITY
DOI:
https://doi.org/10.65164/0ntjje27Keywords:
radar system, DDPM, generative models, radar signal synthesis, aerial object detection, small-sized targets.Abstract
This paper addresses the problem of improving the efficiency of detecting smallsized
aerial objects through the application of diffusion models for the synthesis and reconstruction
of radar signals generated by mobile radar systems. The proposed approach is based on the use of
generative models, including DDPM and score-based models, to expand training datasets, suppress
noise, and enhance the informativeness of radar signatures of objects. The result of the study is the
development of a methodology and a hardware-software module as part of a compact intelligent radar
system, providing increased detection probability and improved classificat ion accuracy of small-sized
targets. The feasibility of implementing the proposed approach using modern electronic components
has been evaluated, and its key characteristics have been determined. A method for detection and
505
recognition of aerial objects is proposed, based on the combined use of diffusion models and deep
learning algorithms, ensuring robustness to noise and limited training data.
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