Effective Ground-Truthing of Supervised Machine Learning for Drone Classification

It has already been shown that multibeam staring radar is able to detect and track low observable targets such as drones due to its high sensitivity [1]. Due to this level of sensitivity, targets that have a similar RCS to drones are also detected and tracked. These are predominantly birds. Birds and drones are similar in several ways such as flight altitude, velocity and manoeuvrability [2] such that discrimination between them is challenging. Hence, there is a need to look for high performing methods of classification, for example, machine learning. Supervised training of machine learning classifiers requires accurately labelled training data. For control targets, such as drones, truth data from the on-board GPS logging can be used for data labelling. However, opportune bird targets require a separate data collection method that enables association with the radar output for a classifier to be effectively trained. This paper shows a method of collecting and displaying ground-truth for small targets onto GoogleEarth so that the radar data can be appropriately used to create accurate training data for a machine learning, drone and bird classifier. Results of classification performance are presented showing high performance that is aided by the availability of more effective truth data.