This paper addresses the problem of recognizing and mitigating impurities in micro-Doppler signals to improve target classification accuracy. Micro-Doppler signatures capture fine movements of objects, but noise, clutter, and environmental interference introduce impurities that degrade classification performance. We propose a hybrid approach combining advanced signal processing and machine learning to detect impurities, filter out interference, and accurately determine the target. Experiments demonstrate the effectiveness of our method on both synthetic and real-world datasets, with improved classification performance in noisy environments.