  The safe operation of drone swarms beyond visual line of sight requires
multiple safeguards to mitigate the risk of collision between drones flying in
hyper localised scenarios. Cooperative navigation and flight coordination
strategies that rely on pre-planned trajectories and require constant network
connectivity are brittle to failure. Drone embedded sense and detect offers a
comprehensive mode of separation between drones for deconfliction and collision
avoidance. This paper presents the first airborne LiDAR based solution for
drone-swarm detection and localisation using 3D deep learning. It adapts and
embeds the PointPillars deep learning neural network on the drone. To collect
training data of close-quarter multi drone operations and safety critical
scenarios, a scenario Digital Twin is used to augment real datasets with high
fidelity synthetic data. The method has been validated in real-world tests. The
trained model achieves over 80% recall and 96% precision when tested on real
datasets. By incorporating a detection-by-tracking algorithm the system can
reliably monitor the separation distance of multiple drones in challenging
environments.
