Manga, AmishaPain, STaylor, J-P2026-01-152026-01-152025979-8-3315-4433-1979-8-3315-4432-4979-8-3315-4434-8DOI: 10.1109/RadarConf2559087.2025.11205061http://hdl.handle.net/10204/14604This research attempts to address the issue of animal poaching by exploring human and animal classification by making use of micro-Doppler data generated by a Frequency-Modulated Continuous Wave (FMCW) radar. Raw data was collected of human and animal species including dogs, horses and cows. Signal processing techniques such as creating range-Doppler and Constant False Alarm Rate (CFAR) maps for detection and using Short-Time Fourier Transform (STFT) for spectrogram generation were applied. Principal Component Analysis (PCA) was used for data reduction. The dataset was classified and evaluated across various target class configurations, comparing the full dataset and its PCA-reduced versions, using Convolutional Neural Network (CNN), k-Nearest Neighbor (kNN), Random Forest (RF) and Support Vector Machine (SVM) models. Following this, a two-stage classification process was implemented. In the first stage, the 4 classifiers were used to distinguish between human and animal. In the second stage, these classifiers differentiated among the specific animal species. The SVM-SVM achieved the highest accuracy at 97.66%, closely matching the 97.50% from the multi-class classification.AbstractenFrequency Modulated Continuous WaveFMCWShort-Time Fourier TransformSTFTMicro-dopplerRadarConference PresentationN/A