Micro-Doppler models of drones, birds, and bird-like drones

Counter-drone systems can confuse drones, birds, and bird-like drones. Their radar micro-Doppler (μ-D) effects can be used to differentiate these targets from each other. Due to the lack of availability of μ-D signature datasets, μ-D return models from quadcopters with spinning rotors, birds with flapping wings, and bird-like drones with flapping wings were implemented for classification purposes. To showcase their use, these models were used to generate a synthetic dataset of spectrograms for binary classification (Drone vs. Bird) and ternary classification (Quadcopter vs. Bird-Like Drone vs. Bird). The classifiers examined were support vector machine (SVM), k-nearest neighbors (KNN), Naïve Bayes, and a convolutional neural network (CNN). All binary classifiers produced at least 92.0% accuracy and at least 93.9% F1 score. All ternary classifiers produced at least 89.5% accuracy and 89.4% F1 score. The classification results indicate the μ-D models possess suitable fidelity for further research and development in drone vs. bird classification.