Assessing Noise Effects on UAV Classification Accuracy With Deep Learning and FPGA Real-Time Processing: A Study Utilizing Radar Digital Twins

The increasing use of unmanned aerial vehicles (UAVs) highlights the need for robust classification systems. This study explores the impact of noise on UAV classification accuracy using radar-based deep learning (DL). A noise-free dataset of range-Doppler maps (RDMs) is generated from digital twin simulations, and noisy datasets are created by adding additive white Gaussian noise (AWGN) across signal-to-noise ratio (SNR) levels from −20 to 10 dB. A DL model, trained on a merged dataset, achieves an accuracy exceeding 98%. Performance evaluation shows accuracy dropping to 34% at −14-dB SNR, with receiver operating characteristic (ROC) curves used for analysis. Inference at the software level is conducted using TensorFlow on a PC within the Vitis-AI Docker environment, achieving an accuracy of 96.39%. The quantized model, when deployed on the KR260 field-programmable gate array (FPGA), attains an accuracy of 94.73%. Although there is a slight drop in accuracy, the hardware (HW) implementation demonstrates impressive performance, supporting a real-time UAV classification system that is effective across different noise levels. Finally, the pretrained model is deployed on an FPGA processing platform to evaluate its effectiveness for real-time classification. By leveraging the FPGA’s real-time processing capabilities, this approach satisfies the stringent speed and accuracy requirements necessary for UAV classification.