## 1. Introduction

[2] Applications of imaging and detection by using scattered field data range from far-field imaging [*Franceschetti and Lanari*, 1999] to near-field imaging by considering microwave [*Bolomey*, 1991, 1995; *Zoughi*, 2000] as well as other electromagnetic frequencies [*Kak and Slaney*, 1988; *Baltes*, 1980]. Many of these imaging applications can be dealt within the same methodological framework. Then it turns out to be very attractive and justified to study numerical approaches able to solve fundamental problems of inverse scattering since any scientific advance in a particular application automatically (or in an indirect way) provides a useful contribution for the progress in related fields.

[3] A brief review of the more recent literature shows that a large number of very effective iterative nonlinear procedures has been proposed. Starting from an initial guess, the parameters of interest are iteratively updated by minimizing a suitably defined cost function involving the measured scattered field data. Generally speaking, two methodological approaches can be highlighted depending on whether the field inside the investigation domain is computed as a solution of the direct problem (in correspondence with the best estimate of the dielectric distribution) at each iteration [*Joachimowicz et al.*, 1991; *Chew and Wang*, 1990; *Franchois and Pichot*, 1997] or as another unknown to be determined during the minimization procedure [*Kleinman and van den Berg*, 1992; *Van den Berg and Abubakar*, 2001]. The IMSA [*Caorsi et al.*, 2003] belongs to the second class of iterative procedures. It is devoted to fully exploit all the available information content of scattered data. Owing to the limited amount of information content in the input data, it would be problematic to parameterize the investigation domain in terms of a large number of pixel values (in order to achieve a satisfying resolution level in the reconstructed image). In order to overcome this drawback, an iterative parameterization of the test domain, performing a synthetic zoom on the region to whom the scatterer belongs, allows to achieve the required reconstruction accuracy only in the “significant” region under test.

[4] Such a technique has yielded very promising results in processing preliminary synthetic test cases [*Caorsi et al.*, 2003]. However, in order to develop a reliable reconstruction method robust to both modeling errors and uncertainties on data, it is mandatory to evaluate its limitations through accurate investigations. Consequently, a better understanding of the operational capabilities of the IMSA requires an extended assessment of the noise robustness as well as a systematic study of the impact of both experimental and model errors. Toward this end, this paper is aimed at providing an assessment of the effects of the major sources of experimental and model noise on the quality of the reconstruction. Accordingly, synthetic as well as laboratory-controlled experiments are taken into account in order to evaluate the effectiveness of the approach in dealing with customized scenarios as well as reference benchmarks.

[5] The paper is organized as follows. First, an outline of the iterative multiscaling approach will be concisely described (section 2). Sections 3 and 4 will present selected representative results for illustrating the effects of the most critical experimental and numerical parameters on the reconstruction accuracy. Final comments and conclusions will be drawn in section 5.