Classification of Drones Using Millimeter-Wave Radar: A Comparative Analysis of Algorithms under Noisy Conditions

This work investigates the performance of different machine learning algorithms in the detection and identification of drones based on radar data from a 60 GHz millimeter-wave sensor. These signals are collected from a bionic bird and two drones, namely DJI Mavic and DJI Phantom 3 Pro, which were represented in complex form to hold amplitude and phase information. First benchmarks used four algorithms, namely LSTM, GRU, CNN, and Transformer, evaluated on noisy data. The main emphasis is on the assessment of algorithm robustness under noisy conditions, including artificial noise types like white noise, Pareto noise, impulsive noise, and multi-path interference. As expected, the Transformer outperformed other algorithms in accuracy, even on noisy data. However, certain noise contexts, especially Pareto noise, showed weaknesses in the effectiveness of the Transformer. For this purpose, we propose a Multimodal Transformer that incorporates more statistical features-skewness and kurtosis-in addition to amplitude and phase data. This resulted in a great improvement in the accuracy of detection, even under difficult noise conditions. Our results demonstrate the importance of noise in processing radar signals and the benefits afforded by a multimodal presentation of data in the problem of detecting UAVs and birds. The presented work sets up a benchmark for state-of-the-art machine learning methodologies for radar-based detection systems, providing valuable insight into methods of increasing the robustness of algorithms to environmental noise.