THMA1: Collecting Big Data of RCS by 3D EM Simulation in WIPL-D suit

Presentation Abstract
Rapid identification of object from RCS signals is important for traffic control, space and defense applications. The problem is typically solved using artificial neural networks. For accurate identification big data of monostatic/bistatic RCS is required. RCS data in a dense grid of directions should be collected for plane wave excitation incoming from numerous directions in a broad frequency range. Measurements cannot be used to collect sufficient data, and even for 3D EM simulation the task is challenging. WIPL-D will demonstrate a number of new options to acquire big data of RCS using examples of interest (birds, drones, vehicles, aircraft).