Drone identification
In this notebook we construct a classifier for identifying drones. Our assumption is that when we reflect a radio pulse off a drone, the reflected signal received back by the observer is a combination of the reflection caused by the drone's body and the reflection caused by the drone's propeller. We compute path signatures for several thousand simulated radio pulses reflected off drone objects with varying propeller locations, before averaging the path signatures. This approach of using expected path signatures aims at characterising the random behaviour in reflected signals. Taking estimates of expected path signatures as our feature vectors, we consider the task of distinguishing between drone and non-drone objects, in addition to predicting the number of rotations per minute (RPM) of the drone's propeller.