Target Classification Based on Micro-Doppler Signatures

This graduate thesis presents a highly significant study on target classification based on micro-Doppler signatures. The paper provides detailed explanations of micro-Doppler characteristics and their applications in target classification, while also examining limitations of existing classification methodologies. We introduce a novel target classification approach that overcomes these limitations through advanced signal processing algorithms, achieving higher classification accuracy and robustness. The implementation involves key functions for feature extraction (such as time-frequency analysis using Short-Time Fourier Transform) and machine learning classifiers (like SVM or CNN architectures). Additionally, we comprehensively detail our experimental design, including dataset preprocessing and parameter optimization procedures, present empirical results, and provide thorough analysis and discussion of findings. We believe this research will contribute substantially to both micro-Doppler signature analysis and the advancement of target classification technologies.