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Two effective approaches for compensating the positioning errors in a near-field-far-field (NF-FF)
transformation technique with spherical scanning for long antennas using a nonredundant number of data are presented.
The transformation technique relies on the nonredundant sampling representations of the electromagnetic fields and on the
optimal sampling interpolation (OSI) expansions, and assumes the antenna under test as enclosed in a prolate ellipsoid, a
source modelling particularly suitable to deal with elongated antennas. In order to evaluate the NF data at the points fixed
by the nonredundant representation from the acquired irregularly spaced ones, the former approach employs the singular
value decomposition method, whereas the latter makes use of an iterative technique. The former can be applied when the
irregularly samples lie on nonuniform parallels, thus allowing to reduce the starting two-dimensional problem into two
independent one-dimensional ones. The latter can be employed also when such a hypothesis does not hold, but requires
the existence of a one-to-one correspondence associating at each uniform sampling point the nearest irregular one. In both
the cases, the NF data needed by a probe compensated NF-FF transformation with spherical scanning are efficiently
evaluated by using an OSI algorithm. Numerical tests assessing the effectiveness of the proposed approaches and their
stability with respect to random errors affecting the NF data are shown.