This dissertation explores the development of a synthetic aperture radar (SAR) imaging and object classification system. Built around a microcomputer, the system integrates radar imaging, image reconstruction, and deep learning model. The radar operates on the Ground-Based Synthetic Aperture Radar (GBSAR) principle, with image reconstruction using the Omega-K algorithm. Various model architectures were compared for object classification, with the Residual Network (ResNet) architecture standing out. The dissertation analyzes models trained on raw radar data versus those trained on reconstructed images. Although reconstructed images are more intuitive, models based on raw data achieve superior results. The ResNet-18 architecture was adapted for raw data, maintaining the horizontal dimension of the data matrix during training. The system enhances energy efficiency using communication between the classification model and the radar. Dynamic adjustment of radar parameters reduces the need for detailed measurements by 73% while maintaining high classification accuracy. The potential of incorporating additional information to the model based on dual linear polarization imaging was also explored. Approaches for combining matrices recorded using horizontal and vertical polarizations were described, as well as a model architecture that processes these matrices separately before merging feature vectors before fully connected layer. Iterative testing demonstrated that additional information obtained using matrices recorded with other polarization improves model accuracy.