Microwave imaging of small scatterers is an inverse scattering problem, and recently, the MUSIC algorithm has been proposed to solve this type of problem. The MUSIC algorithm, by assuming that the number of targets is a priori known, can locate the scatterers from the peaks of the well-known pseudospectrum. The noise and multiple scattering create ambiguity to detect the number of targets. Usually, information-based algorithms such as Akaike information criterion (AIC) and minimum description length (MDL) are employed for source number estimation. However, in the cases of low signal-to-noise ratio (SNR) and close targets, the performance of these methods is seriously degraded. In the present work, we propose a two-step approach to enumerate the scatterers in microwave imaging applications for cases where traditional methods fail. Firstly, the MUSIC algorithm is applied to locate all possible targets by assuming the maximum number of targets, and secondly, we can discriminate between the real and unreal targets by using a novel formula that acts as a spatial filter. The efficiency of the proposed method has been examined through various simulation tests using numerical and experimental datasets, and the results verify that the method can accurately specify the location and the number of scatterers in 2D microwave imaging applications.
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