In this work, the nonparametric kernel prediction will be considered for spatial stochastic processes when a stochastic sampling design is assumed for selection of locations. We will prove that under rather general conditions, the mean-squared prediction error tends to be negligible as the sample size increases. However, use of the optimal bandwidth demands the estimation of unknown quantities, whose accurate approximation can often be difficult in practice. Hence, alternative cross-validation approaches will be provided for the selection of both local and global bandwidths. Numerical studies were carried out in order to analyse the performance of the nonparametric predictor for both simulated and real data.

A unique multireflector bifocal antenna design for real-time standoff imaging applications is presented in this work. The antenna is used in combination with a 300 GHz radar sensor to acquire images at 8 m over a large (80 × 50 cm) field of view (FoV). The increased FoV is obtained by the use of an antenna system composed by a bifocal elliptical Gregorian with an extended subreflector to allow for fast scanning of a beam spot. The scanning is achieved by the combination of the rotation and vertical tilting of a flat small secondary mirror. The design, simulations, prototype description and experimental results of a manufactured working prototype are presented.