Global Navigation Satellite System (GNSS) data has become a stable of modern life, whether delivered through a smartphone, vehicle, or as part of an industrial dataset. The expectation of reliable position and time information, irrespective of the environment, becomes challenging in tunnels and canyons with significant multipath, whether natural or urban. An open dataset of Lidar, Computer Vision, Inertial measurement, and GNSS data is presented, combined with preliminary analysis of the dataset on a selected recording, considering GPS coarse acquisition, lidar and computer vision based navigation together with model based channel reconstruction. Lidar and video based techniques demonstrate positioning error as low as 3.8m root mean square error, together with GNSS acquisition and signal blockage alignment with computer vision and lidar maps.

The development of the networked battlespace as a concept, and the wealth of wired and wireless communications to support it have opened the door to truly multidimensional radar systems. Whether as a static network of radars coordinating to provide national coverage, or a local network for UAV's providing a swarm aperture for rapidly reconfigurable airborne radar, there is an urgent need for a flexible radar channel model to empower the engineer and scientist to develop new systems, sensing algorithms and concepts of operation. LyceanEM, an open source electromagnetics model is presented as a solution to this issue, allowing for multi source, heterogeneous antenna arrays to be simulated together with a radar target.