One important task in the design of an antenna is to carry out an analysis to find out the characteristics of the antenna that best fulfills the specifications fixed by the application.After that, a prototype is manufactured and the next stage in design process is to check if the radiation pattern differs from the designed one. Besides the radiation pattern, other radiation parameters like directivity, gain, impedance, beamwidth, efficiency, polarization, etc. must be also evaluated. For this purpose, accurate antenna measurement techniques are needed in order to know exactly the actual electromagnetic behavior of the antenna under test. Due to this fact, most of the measurements are performed in anechoic chambers, which are closed areas, normally shielded, covered by electromagnetic absorbing material, that simulate free space propagation conditions, due to the absorption of the radiation absorbing material. Moreover, these facilities can be employed independently of the weather conditions and allow measurements free from interferences. Despite all the advantages of the anechoic chambers, the results obtained both from far-field measurements and near-field measurements are inevitably affected by errors. Thus, the main objective of this Thesis is to propose algorithms to improve the quality of the results obtained in antenna measurements by using post-processing techniques and without requiring additional measurements.First, a deep revision work of the state of the art has been made in order to give a general vision of the possibilities to characterize or to reduce the effects of errors in antenna measurements.Later, new methods to reduce the unwanted effects of four of the most commons errors in antenna measurements are described and theoretical and numerically validated.The basis of all them is the same, to perform a transformation from the measurement surface to another domain where there is enough information to easily remove the contribution of the errors. The four errors analyzed are noise, reflections, truncation errors and leakage and the tools used to suppress them are mainly source reconstruction techniques, spatial and modal filtering and iterative algorithms to extrapolate functions. Therefore, the main idea of all the methods is to modify the classical near-field-to-farfield transformations by including additional steps with which errors can be greatly suppressed. Moreover, the proposed methods are not computationally complex and, because they are applied in post-processing, additional measurements are not required.
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