ML Approach for Classification of Impaired Radar Signals using Feature Extraction
DOI:
https://doi.org/10.48047/6052wk51Keywords:
Keywords: Radar signals, Feature extraction, Machine learning, XGBoost model.Abstract
Many different industries, including aerospace, defense, meteorology, and automotive, rely heavily on radar systems for their numerous applications. On the other hand, these systems frequently experience signal impairments as a result of noise, interference, and signal deterioration, which can put their accuracy and reliability at risk. As a result, the purpose of this study is to improve the classification of degraded radar signals by utilizing sophisticated approaches for feature extraction and machine learning (ML). Traditional methods, such as threshold-based, statistical approaches, and manual analysis, have a limited capacity to deal with thecomplexity and diversity of radar signal impairments. As a result, these methods frequently result in inadequate performance and flexibility.
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