point tends to decrease. This suggests that the information is not evenly distributed along the observation domain, but is more concentrated in front of the source. The relevant consequence is that the Nyquist distance tends to change along the line of observation. In accordance with the intuitive discussion outlined above, and following the reference [22], instead of directly considering the field on the observation curve, we consider the reduced field F [19, 22]: F(E)=E(E) (2.8) in which we introduce the parameterization function along the observation curve su = s(r), and the phase function (§) = 4(s(r)) (see Fig. 2.6). As discussed above, these functions take into account the effect of the geometry. The parameter- ization function changes the distance between two points on the curve, while the phase function allows to subtract the phase specified by y from the field. Our final goal is the identification of su and y functions that give the best bandlimited approximation of the field. Mathematically, this requires to identify the su and y functions that minimize the distance between the reduced field F and the bandlimited approximation of the reduced field Fw, wherein w is the bandwidth. The first step is the evaluation of the spatial bandlimited version of the reduced field [19,21]: (2.9) M. D. Migliore wherein w' is the angular spatial bandwidth, G is the reduced Green's function [22] and G w is the bandlimited approximation of G This formula deserves some comments from a physical point of view. In particular, it shows that the bandlimited property of the field is directly related to a bandlimited property of Green's function. Indeed, Green's function acts as a "spatial filter" that limits the amount of information that can be collected on the observation domain. The band limitation error on G for large observation distance is [19, 22]: (2.10) and involves integrals whose kernel includes exponential functions with imaginary argument: (2.11) wherein These kinds of integrals are quite common in electromagnetic d