Hybrid Analog-Digital Sensing Approach for Low-power Real-time Anomaly Detection in Drones

With the rapid growth of the use of Machine Learning (ML) techniques in Unmanned Aerial Vehicles (UAVs), there is an opportunity to use ML techniques to detect and prevent anomalous behavior in drones. However, limited drone power thwarts successful implementation of contemporary power-hungry ML techniques. Therefore, we propose a hybrid analog-digital system to solve the problem of continuous anomaly detection. In this paper, a series of pure analog ML methods including SVMs (linear, polynomial, Radial Basis Function (RBF) and Sigmoid kernels) as well as pure analog fully-connected Neural Network (NN) are presented. We validate our method with sensor data from a series of drone experiments to detect and identify causes of failure in real-time. The results show that RBF kernel provides at least 88.17 % and at most 99.99 % accuracy under different time window and crash-like scenarios with an extremely low False Negative (FN) ratio with sensor data especially in the z-axis.