Optimal separation of polarized signals by quaternionic neural networks

Statistical description of polarized signals is proposed in terms of proper quaternionic random processes. Within this framework, the intrinsic nature of such signals is captured well. Simulation results show the ability of quaternionic approach (statistical model and processing) to perform better separation of polarized signals than real-valued neural networks can do.