Adaptive Polarized Waveform Design for Target Tracking using Electromagnetic Vector Sensors

We develop an adaptive waveform design method for target tracking. In our method, at each time step, we optimally select the parameters, including the polarization of the transmitted signal waveform to improve the tracking accuracy. An array of electromagnetic vector sensors is employed to fully recover the polarimetric information from the reflected signals. We derive our approach under a framework of sequential Bayesian filtering. We apply a sequential Monte Carlo method to manipulate the nonlinear and non-Gaussian state and measurement models. We design a criterion for the waveform optimization based on a posterior Cramer-Rao bound.