Adaptation of Multi-target Tracker Using Neural Networks in Drone Surveillance Radar

In this paper, we present a study on adapting a radar multi-target tracker using neural networks to enhance its performance against agile, maneuvering, targets such as drones. In particular, a network dynamically adjusts the process noise of the target motion model based on the normalised filtering innovations. Different neural networks are evaluated for this task, namely fully connected, recurrent and convolutional networks. They are trained on representative simulated data, including waypoint-driven trajectories which are common with (semi-) autonomous systems, e.g. small unmanned air systems. Results from synthetic radar data demonstrate the potential benefits of adapting a multi-target tracker with a low-complexity recurrent neural network, albeit the modest improvements it achieves.